Image file gathering method and device, electronic equipment and storage medium

Abstract

The invention discloses an image file gathering method and device, electronic equipment and a storage medium, and the method comprises the steps: determining at least two connected components in an image to be subjected to file gathering, and determining redundant edges in the connected components according to the degree of each image node in the connected components; performing image file gathering according to the to-be-file-gathered image after the redundant edges are deleted to obtain at least two initial image files; and performing primary combination on the initial image files corresponding to the same connected component, and performing secondary combination on the initial image files corresponding to different connected components and / or the image files after the primary combination to obtain at least two target image files. According to the method, memory consumption and computing resource waste can be reduced, and the file gathering efficiency and accuracy are improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to an image archiving method, device, electronic device and storage medium. Background Art

[0002] With the rapid development of big data technology, the volume and complexity of image data are growing exponentially. A graph is a mathematical structure composed of vertices and edges. In image clustering scenarios, images can be used as graph vertices, similarity relationships between images as edges, and the graph as input to the clustering algorithm.

[0003] As graphs continue to grow in size, storing and processing graph structures not only increases memory load but also significantly increases computation time. Furthermore, there is some redundant information in the graph structure, especially for connected components. A connected component is a subgraph consisting of a set of vertices in the graph. Any two vertices in a connected component are directly or indirectly reachable, while vertices within a connected component are not directly connected to vertices in other connected components. The many edges in a connected component do not significantly affect the clustering results, but they can lead to excessive memory consumption, low computational efficiency, and a waste of resources. Summary of the Invention

[0004] The present invention provides an image aggregation method, device, electronic device and storage medium to reduce memory consumption and computing resource waste, and improve the efficiency and accuracy of aggregation.

[0005] In a first aspect, an embodiment of the present invention provides an image aggregation method, the method comprising:

[0006] Determine at least two connected components in the graph to be aggregated, and determine redundant edges in the connected components based on the degrees of the image nodes in the connected components;

[0007] Perform image clustering according to the to-be-clustered graph after removing redundant edges, to obtain at least two initial image files;

[0008] Initial image files corresponding to the same connected component are initially merged, and initial image files corresponding to different connected components and / or image files after the initial merger are secondary merged to obtain at least two target image files.

[0009] In a second aspect, an embodiment of the present invention further provides an image aggregation device, the device comprising:

[0010] A redundant edge determination module is used to determine at least two connected components in the graph to be aggregated, and determine redundant edges in the connected components based on the degrees of the image nodes in the connected components;

[0011] An initial image archive determination module is used to perform image aggregation based on the to-be-aggregated image graph after redundant edges are deleted, to obtain at least two initial image archives;

[0012] The target image file determination module is used to perform an initial merger of the initial image files corresponding to the same connected component, and to perform a secondary merger of the initial image files corresponding to different connected components and / or the image files after the initial merger, to obtain at least two target image files.

[0013] In a third aspect, an embodiment of the present invention further provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, an image archiving method as described in any one of the embodiments of the present invention is implemented.

[0014] In a fourth aspect, an embodiment of the present invention further provides a storage medium storing computer-executable instructions, which, when executed by a computer processor, are used to execute the image archiving method as described in any one of the embodiments of the present invention.

[0015] The technical solution of the embodiment of the present invention determines the connected components of the graph to be clustered, determines the redundant edges in the connected components based on the degree of each image node in the connected components, deletes the redundant edges, and then clusters the graph to be clustered. After obtaining the initial image archive, the initial image archives within the same connected component are first merged, and then the image archives between different connected components are merged to obtain the final target image archive. This solves the problems of excessive memory consumption, low computing efficiency, and waste of computing power caused by large graph scales and the presence of redundant edges in the prior art. The embodiment of the present invention can reduce memory consumption and waste of computing resources, and improve the efficiency and accuracy of clustering.

[0016] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 This is a flow chart of an image aggregation method provided by the first embodiment of the present invention;

[0019] Figure 2is a schematic diagram of a connected component provided by the first embodiment of the present invention;

[0020] Figure 3 is a schematic diagram of a key image node set and an adjacent image node set provided by the first embodiment of the present invention;

[0021] Figure 4 This is a schematic diagram of redundant edge determination provided by the first embodiment of the present invention;

[0022] Figure 5 This is a schematic diagram showing the effect of the file merging order on the file aggregation result provided by the first embodiment of the present invention;

[0023] Figure 6 This is a flow chart of another image aggregation method provided by the second embodiment of the present invention;

[0024] Figure 7 is a schematic diagram of an initial image file before redundant edges are deleted, provided by the second embodiment of the present invention;

[0025] Figure 8 This is a schematic diagram of the effect of centroid on image file merging after redundant edges are deleted, provided by the second embodiment of the present invention;

[0026] Figure 9 This is a schematic structural diagram of an image aggregation device provided by the third embodiment of the present invention;

[0027] Figure 10 This is a structural diagram of an electronic device provided in Example 4 of the present invention. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices. In the embodiments of the present application, certain software, components, models and other existing solutions in the industry may be mentioned, and they should be considered as exemplary. Their purpose is merely to illustrate the feasibility of the implementation of the technical solution of the present application, but it does not mean that the applicant has or will necessarily use the solution.

[0030] The acquisition, transmission, storage, use, and processing of data in the technical solution of this application comply with the relevant provisions of national laws and regulations.

[0031] Example 1

[0032] Figure 1 A flowchart of an image archiving method is provided for the first embodiment of the present invention. This embodiment is applicable to situations where image archiving is performed based on a graph. The method can be executed by an image archiving device, which can be implemented in the form of hardware and / or software. The image archiving device can be configured in an electronic device or a server.

[0033] like Figure 1 As shown, the method includes:

[0034] S110: Determine at least two connected components in the graph to be aggregated, and determine redundant edges in the connected components based on the degrees of the image nodes in the connected components.

[0035] A graph is a concept in graph theory, consisting of a set of nodes and edges. Nodes are objects, and edges describe the relationship between objects corresponding to the nodes at both ends. In this embodiment, the graph for clustering is a graph whose nodes represent images. It serves as input for image clustering. The graph for clustering consists of image nodes and the edges between them. The edges between image nodes represent the similarity between the image nodes.

[0036] A connected component refers to a subgraph in the graph to be aggregated, including a group of image nodes. The image nodes within a connected component are directly or indirectly connected, and the image nodes between a connected component and other connected components are not connected. Figure 2 A schematic diagram of connected components is provided, such as Figure 2 As shown, circles represent image nodes, and lines between circles represent edges between image nodes. Figure 2 Three sets of examples of connected components are provided.

[0037] Connected components in the graph to be clustered can be identified using methods such as the DFS (Depth-First Search) algorithm or the BFS (Breadth-First Search) algorithm. Taking the DFS algorithm as an example, an unvisited image node is identified in the graph to be clustered. Starting from this image node, a depth-first search is performed along the edges, marking all visited image nodes. When the search is no longer possible, all image nodes visited in this search constitute a connected component. Then, an unvisited image node is identified, and the above process is repeated until all image nodes in the graph to be clustered have been visited.

[0038] In this embodiment, after each connected component is determined, the corresponding relationship between each connected component and the image nodes inside it can be obtained.

[0039] Graphs are further divided into directed graphs and undirected graphs. An undirected graph refers to a bidirectional relationship represented by an edge. If there is an edge between image node A and image node B in an undirected graph, then one can reach image node B from image node A, and vice versa. A directed graph refers to a directional relationship represented by an edge. If there is an edge pointing from image node A to image node B, then one can reach image node B from image node A, but not necessarily directly from image node B to image node A. On this basis, in an undirected graph, degree refers to the number of edges connected to an image node. In a directed graph, degree can be divided into in-degree and out-degree. In-degree refers to the number of edges pointing to the image node, and out-degree refers to the number of edges starting from the image node. For a directed graph, the degree in this embodiment specifically refers to in-degree, which is used to determine the redundant edges in the connected component.

[0040] Determine the degree of each image node in the connected component. For an undirected graph, you can traverse each image node in the connected component and count the number of all edges connected to the image node. For a directed graph, you can use the adjacency matrix method or the adjacency table method to determine the in-degree of the image node. Specifically, taking the adjacency matrix method as an example, the adjacency matrix A is used to represent the graph to be assembled, and A[i][j] indicates whether there is an edge from image node i to image node j. For example, A[i][j]=1 indicates that there is an edge, and A[i][j]=0 indicates that there is no edge. The in-degree of image node k is obtained by counting the sum of the elements in the kth column of the adjacency matrix.

[0041] Redundant edges refer to edges that do not significantly affect the aggregation results. Deleting the redundant edges will not affect the topological structure characteristics of the graph to be aggregated, nor will it affect the composition of the connected components where the redundant edges are located.

[0042] According to the degree of each image node in the connected component, the redundant edges in the connected component are determined. In an optional embodiment, the image nodes can be sorted and processed in sequence according to the order of the degree of each image node in the connected component from large to small. Take the image node with the largest degree among the currently unprocessed image nodes, and process each edge connected to it (for a directed graph, specifically the edge pointing to this image node), as well as other image nodes at the other end of each edge. Determine whether there are other paths to reach other image nodes at the other end of this edge after deleting this edge. If so, then treat this edge as a redundant edge. Process each image node in sequence until all image nodes with a degree greater than or equal to 2 have been processed.

[0043] Determining redundant edges in a connected component based on the degree of each image node in the connected component may, in another optional embodiment, further include:

[0044] A1. Determining key image nodes from among the image nodes of the connected components based on the degree of each image node, forming a key image node set; and determining adjacent image nodes adjacent to the key image nodes, forming an adjacent image node set;

[0045] A2. If it is determined that the key image node and the adjacent image nodes form a loop, then the edges in the loop except the edges between the key image nodes are taken as candidate edges;

[0046] A3. Determine the similarity between the two image nodes corresponding to the candidate edges, and use the candidate edge with the lowest similarity as a redundant edge;

[0047] A4. Mark each image node in the key image node set and the adjacent image node set as processed, and repeatedly perform the operation of determining the key image nodes in the unprocessed image nodes of the connected component until all image nodes in the connected component are processed and all redundant edges in the connected component are obtained.

[0048] Among them, the key image node refers to the image node that has a higher status in information dissemination, resource sharing, etc. and plays a key hub role among the image nodes of the connected components.

[0049] Based on the degree of each image node in the connected component, a key image node is determined among the image nodes of the connected component. In an optional embodiment, the image node with the highest degree can be selected from the image nodes of the connected component, and the link including the image node is determined based on the edges connected to the image node. For each link, the following conditions are judged in sequence: whether a closed loop can be formed, whether the total number of image nodes in the link is the least, and whether the total number of degrees of image nodes in the link is the highest. When the above three conditions are met at the same time, each image node on the link is regarded as a key image node and added to the key image node set.

[0050] According to the degree of each image node in the connected component, key image nodes are determined from each image node in the connected component to form a key image node set. In another optional embodiment, the following may be included:

[0051] A11. Among the image nodes of the connected components, the image node with the highest degree of certainty is taken as the key image node;

[0052] A12. Determine the image node with the highest similarity among the image nodes connected to the key image node as the key image node, and repeat the process until a stopping condition is satisfied.

[0053] The stopping condition includes that the number of key image nodes is greater than or equal to a preset node number threshold, and / or a loop is formed between the key image nodes;

[0054] A13. Group the key image nodes into a key image node set.

[0055] In this embodiment, for a determined graph to be aggregated, the degree of each image node and the similarity of the edges between image nodes are known. Therefore, for a connected component, the image node with the highest degree is first determined among the image nodes in the graph and is used as the key image node.

[0056] Among the edges connected to the key image node, the image node corresponding to the edge with the highest similarity is selected as the new key image node. Continue selecting the image node corresponding to the edge with the highest similarity among the edges connected to the new key image node as the new key image node. Repeat this process until a preset threshold number of nodes (e.g., 20) has been selected, or a loop is formed between the selected key image nodes.

[0057] It should be noted that if there are at least two image nodes with the highest degree and the same degree, since each image node of the connected component needs to be traversed once when determining redundant edges, an image node can be randomly selected from each image node as the key image node. Similarly, if there are at least two image nodes with the highest similarity and the same similarity, an image node can be randomly selected from each image node as the key image node.

[0058] Adjacent image nodes refer to image nodes that are directly connected to key image nodes through edges. Adjacent image nodes adjacent to key image nodes can be determined directly based on the edges corresponding to each key image node.

[0059] Figure 3 A schematic diagram of a key image node set and an adjacent image node set is provided, such as Figure 3 As shown in the figure, for the connected component, the degree of each image node is counted. There are two image nodes with a degree of 5 in the connected component. The image node on the left is selected as the key image node. The image node with the highest similarity among the image nodes connected to it is found as the key image node. The image nodes with the highest similarity are searched in sequence until Figure 3 The key image nodes in the image form a loop. Figure 3 The V1 in is used to represent the key image node set. Figure 3 After obtaining the key image node set, the image nodes closest to each key image node in the key image node set are found as adjacent image nodes based on the edges of each key image node in the key image node set. Figure 3 The V2 in is used to represent the set of adjacent image nodes. Figure 3 Indicated by a purple circle.

[0060] As can be understood, since key image nodes have a higher status in information dissemination and resource sharing than other image nodes, and play a key hub role, edges between key image nodes are retained to ensure that the topological structure of the connected components remains unchanged. This embodiment determines redundant edges by determining the edges between key image nodes and adjacent image nodes, as well as the edges between adjacent image nodes.

[0061] Specifically, if a key image node and an adjacent image node form a loop, there are redundant edges in the loop. It is understandable that, for a loop, removing one of the edges will not affect the topological structure, and the image nodes can still be directly or indirectly reachable. Therefore, there must be redundant edges in the loop. Because the loop is formed by key image nodes and adjacent image nodes, key image nodes have a higher status than adjacent image nodes, and the edges between key image nodes need to be retained. Therefore, in this embodiment, the edges between key image nodes and adjacent image nodes in the loop and / or the edges between adjacent image nodes are used as candidate edges.

[0062] Furthermore, determining whether a loop is formed between a key image node and adjacent image nodes can be performed by: an edge exists between an adjacent image node and at least two key image nodes, and / or an edge exists between adjacent image nodes. If either of these two conditions is met, it can be considered that a loop exists between the key image node and the adjacent image node.

[0063] Among the candidate edges, the one with the smallest similarity between the image nodes at both ends is selected as the redundant edge. Figure 4 is Figure 3 Based on the key image nodes and adjacent image nodes determined in , a schematic diagram of redundant edge determination is provided, such as Figure 4 As shown in Figure 2, the key image nodes and adjacent image nodes form a loop. In loop 1, the edge between the two key image nodes cannot be used as a redundant edge. The similarities between the two key image nodes and the adjacent image nodes are 0.7 and 0.8, respectively. Therefore, the edge with a similarity of 0.7 is deleted as a redundant edge. In loop 2, the edge between the two key image nodes cannot be used as a redundant edge. The similarities between the two key image nodes and the adjacent image nodes are 0.8 and 0.7, respectively. The similarity between the two adjacent image nodes is 0.6. Therefore, the edge between the two adjacent image nodes is deleted as a redundant edge. In loop 3, the similarities between the key image node and the two adjacent image nodes are 0.7 and 0.8, respectively. The similarity between the two adjacent image nodes is 0.9. Therefore, the edge with a similarity of 0.7 between the key image node and the adjacent image node is deleted as a redundant edge.

[0064] In this embodiment, after redundant edges are identified in the loop formed by the current batch of key image nodes and adjacent image nodes, each redundant edge is deleted. Simultaneously, each key image node and each adjacent image node is marked as processed. Unprocessed image nodes are identified from each image node of the connected component. Key image nodes and adjacent image nodes are re-identified from each unprocessed image node. Redundant edges are then determined using the same method as described above until all image nodes of the connected component are processed.

[0065] In this embodiment, based on the degree of the image nodes in the connected components, dynamic deletion of redundant edges is achieved. While completely retaining the topological structure and connection characteristics between the image nodes, redundant information in the graph to be clustered can be deleted, the processing process of the graph to be clustered is optimized, redundant calculations and memory storage requirements are reduced, the clustering efficiency and scale are improved, and the demand for accurate clustering under large-scale complex data is met.

[0066] S120 , performing image aggregation based on the graph to be aggregated after redundant edges are deleted, to obtain at least two initial image files.

[0067] In this embodiment, image clustering can be achieved based on the to-be-clustered graph after redundant edges have been removed using a clustering algorithm or clustering model. Specifically, the clustering algorithm may include the Louvain algorithm, the Infomap (Information-theoretic Map Equation) algorithm, the spectral clustering algorithm, or the label propagation algorithm, etc. This embodiment does not limit the specific type of clustering algorithm. The clustering model can be trained based on a graph convolutional neural network or a variational graph autoencoder, etc. This embodiment does not limit the specific type of model.

[0068] In this embodiment, the image files obtained by clustering the image after removing redundant edges are used as the initial image files, which are then merged. The benefits of merging the initial image files are that they can reduce the impact of noise, optimize cluster boundaries, enhance the semantic consistency of the clustering results, and improve the accuracy and stability of the clustering.

[0069] S130 , performing a primary merger on initial image files corresponding to the same connected component, and performing a secondary merger on initial image files corresponding to different connected components and / or image files after the primary merger, to obtain at least two target image files.

[0070] In particular, since the clustering algorithm or clustering model generally does not generate image files spanning more than two connected components when clustering a graph to be clustered, that is, one initial image file corresponds to one connected component, and one connected component may contain one or more initial image files. Therefore, in this embodiment, the initial image files within the same connected component are initially merged. After the initial image files within each connected component are merged, the image files between connected components (which may be either the initial image files or the image files after the initial merger) are then merged for a second time. The resulting target image file is the final clustering result.

[0071] In this embodiment, the initial image files within the same connected component are initially merged, and then the image files between different connected components are merged for the second time. The reason for this arrangement is that the merging result of the image files is affected by the merging order and is relatively random. Figure 5 A schematic diagram of the effect of the file merging order on the file aggregation results is provided, such as Figure 5 As shown in the figure, for the four initial image files A, B, C and D, if AB and CD are merged first, the final result is two image files; if BC is merged first, the final result is three image files. Figure 5 It can be seen that different merging orders will result in different image file merging results.

[0072] Therefore, in this embodiment, the image files within the connected components are initially merged, and then the image files across connected components are merged again. This multi-level file merging method based on the node position structure fully utilizes the position information of the image nodes in the image files, greatly reducing the disturbance of the randomness of the merging order on the final aggregation result, avoiding the adverse impact of early merging results on subsequent merging results, improving the stability and robustness of the file merging process, and to a certain extent, moving the merging results from the local optimum to the global optimum. As a result, the final aggregation result obtained after multi-level merging can enhance information retention capabilities.

[0073] The technical solution of the embodiment of the present invention determines the connected components of the graph to be clustered, determines the redundant edges in the connected components based on the degree of each image node in the connected components, deletes the redundant edges, and then clusters the graph to be clustered. After obtaining the initial image archive, the initial image archives within the same connected component are first merged, and then the image archives between different connected components are merged to obtain the final target image archive. This solves the problems of excessive memory consumption, low computing efficiency, and waste of computing power caused by large graph scales and the presence of redundant edges in the prior art. The embodiment of the present invention can reduce memory consumption and waste of computing resources, and improve the efficiency and accuracy of clustering.

[0074] Example 2

[0075] Figure 6 This is a flow chart of another image archiving method provided in the second embodiment of the present invention. Based on the above embodiments, this embodiment of the present invention further specifies the processes of the initial merging and secondary merging of image archives.

[0076] like Figure 6 As shown, the method includes:

[0077] S210: Determine at least two connected components in the graph to be aggregated, and determine redundant edges in the connected components based on the degree of each image node in the connected components.

[0078] S220 , performing image aggregation based on the graph to be aggregated after redundant edges are deleted, to obtain at least two initial image files.

[0079] The process of determining the connected components in the graph to be clustered, determining the degree of each image node in the connected components, determining redundant edges within the connected components based on the degree of each node, and clustering the images has been explained in the above examples and will not be repeated here in this embodiment.

[0080] S230 , determining the centroids of the initial image files corresponding to the same connected component, and determining two candidate initial image files with the highest similarity in the initial image files based on the centroids of the initial image files.

[0081] Among them, the centroid is the center point of the initial image archive, which can summarize the typical characteristics of the data in the initial image archive, accurately reflect the information of the archive, and provide a reliable reference for archive merging.

[0082] Determine the centroid of each initial image file corresponding to the same connected component. Specifically, the feature vector corresponding to each image node in the initial image file can be extracted and averaged to obtain the file centroid.

[0083] It's understandable that after deleting redundant edges, while the topological structure and connectivity of the connected components remain intact, the clustering algorithm will randomly select a starting image node and, with a certain probability, randomly select an adjacent image node to move to during image clustering, thereby traversing the clustering graph. This process is called a "random walk." During this random walk, some image nodes are visited less frequently than before the redundant edges were deleted. If this image node happens to serve as an important relay point in the random walk, a large image archive before the redundant edges were deleted may be divided into multiple small image archives after the deletion.

[0084] Therefore, in this embodiment, the initial image files within the same connected component are merged based on the centroid, which can compensate for the impact of deleting redundant edges on the clustering result and improve the clustering accuracy.

[0085] Furthermore, the centroids of the initial image files corresponding to the same connected component are determined, including:

[0086] B1. For each initial image file corresponding to the same connected component, determine whether the target image node exists in the current initial image file;

[0087] The target image node is the image node corresponding to the redundant edge before deleting the redundant edge;

[0088] B2. If it is determined that the target image node exists in the current initial image file, then the node weight of the target image node is determined according to the number of redundant edges corresponding to the target image node;

[0089] B3. Determine the centroid of the current initial image file based on the node weight of the target image node.

[0090] Based on the above embodiments, this embodiment proposes a weighted centroid determination method based on redundant edge deletion, so that the centroid is more biased towards the image nodes from which the redundant edges have been deleted, thereby compensating for the negative impact of deleting redundant edges on the centroid calculation. It can simulate the calculation of the real centroid when the redundant edges are not deleted, which is conducive to improving the accuracy of subsequent image file merging.

[0091] Specifically, before deleting redundant edges, the image nodes at both ends of the redundant edges can be marked as target image nodes. Alternatively, the number of redundant edges corresponding to the image nodes can be counted by setting a missing degree parameter. Every time a redundant edge is determined, the missing degree of the image nodes corresponding to both ends is increased by one. For each image node in the current initial image file, if the missing degree is greater than 0, it will be used as the target image node. The advantage of this setting is that the integrity of the image node can be evaluated by the number of deleted redundant edges corresponding to the image node, which facilitates the subsequent calculation of the weighted centroid.

[0092] In this embodiment, for the target image node, the node weight is calculated based on its missing degree (that is, the number of redundant edges), and for other image nodes in each image node except the target image node, the node weight is determined using a conventional node weight calculation method.

[0093] The node weight of the target image node is determined based on the number of redundant edges corresponding to the target image node. In an optional embodiment, taking the number of redundant edges corresponding to the target image node represented by the missing degree as an example, the edge loss ratio of the target image node can be calculated based on the missing degree and degree of the target image node: T1 / T1+T2, where T1 represents the missing degree and T2 represents the degree. A mapping relationship between different edge loss ratio intervals and node weights is pre-set, and the node weight corresponding to the interval in which the edge loss ratio of the current target image node falls is used as the node weight of the current target image node.

[0094] The node weight of the target image node is determined according to the number of redundant edges corresponding to the target image node. In another optional embodiment, still taking the number of redundant edges corresponding to the target image node represented by the missing degree as an example, specifically, it can be calculated by the following formula: ,in, represents the node weight of the target image node, , T1 represents the missing degree, and T2 represents the degree. According to the above formula, the larger L is, the greater the node weight of the target image node is, which means that the centroid will be more biased towards the characteristics of the target image node.

[0095] Figure 7 A schematic diagram of the initial image archive before removing redundant edges is provided. Figure 8 exist Figure 7 Based on this, a schematic diagram of the effect of centroid on image file merging after removing redundant edges is provided. Figure 8 As shown in the figure, after deleting redundant edges, the clustering result is two small initial image files, A, B, and C are one initial image file, and DH is one initial image file, represented by green and purple respectively. On this basis, if the centroid is calculated by the mean of the eigenvector, the two small initial image files cannot be merged when the files are merged due to the large distance between the centroids. If the centroid is calculated by weighting based on the missing degree of the image nodes, the centroid will be more biased towards the image nodes C and E with larger missing degrees, so that when the image files are subsequently merged, the two small initial image files can be more easily merged into a large image file, which is closer to the image file clustering result before deleting redundant edges.

[0096] In this embodiment, the information integrity of the image nodes is evaluated based on the missing degree of the image nodes, the image node weights are changed, and the calculation of the image archive centroid is optimized, which can effectively compensate for the impact of the deletion of redundant edges on the image archive centroid characteristics, so that the calculated image archive centroid can restore the state before the redundant edges are deleted, accurately reflect the distribution characteristics of the original data, and thus help to improve the accuracy of subsequent archive merging.

[0097] In this embodiment, for each initial image file in the same connected component, the similarity between each pair of initial image files is calculated based on the centroid obtained by weighted calculation, and then the merging judgment is performed in order from large to small based on the similarity between the initial image files.

[0098] Specifically, based on the centroid of each initial image file, the similarity between each pair of initial image files is calculated. The similarity between the two initial image files can be represented by the Euclidean distance, Manhattan distance, Chebyshev distance or cosine similarity of the centroids of the two initial image files, etc. This embodiment does not limit this.

[0099] S240: Determine a merging threshold that matches the two candidate initial image files according to a preset first merging threshold and a second merging threshold.

[0100] The first merging threshold is greater than the second merging threshold. The first merging threshold is the maximum threshold for merging files within the connected component, and the second merging threshold is the minimum threshold for merging files within the connected component.

[0101] In this embodiment, building on multi-level merging, a method is proposed for dynamically adjusting the merging threshold when merging files within connected components. By dynamically adjusting the merging threshold for different initial image files to be merged, the merging strength can be controlled at the micro level, further optimizing the merging results.

[0102] Based on the pre-set first merging threshold and second merging threshold, the merging threshold for matching the two candidate initial image archives is determined. In an optional embodiment, the merging threshold for matching the two candidate initial image archives can be determined within the interval from the second merging threshold to the first merging threshold based on the missing degree of the target image node in the two candidate initial image archives. The greater the missing degree of the target image node, the smaller the merging threshold for matching the two candidate initial image archives. This can be achieved by pre-setting a mapping relationship between different missing degree intervals and merging thresholds, or by a mathematical formula, which is not limited in this embodiment.

[0103] Understandably, according to Figure 8 It can be seen that the centroid of the initial image file will be more biased towards the image node with a higher missing degree. On this basis, setting a smaller merging threshold for the two candidate initial image files can make it easier for the two candidate initial image files to be merged into one image file, thus being closer to the image clustering effect before deleting redundant edges.

[0104] Determining a merging threshold that matches two candidate initial image files based on a preset first merging threshold and a second merging threshold may, in another optional embodiment, further include:

[0105] C1. Determine the position coefficients between the candidate initial image files according to the node positions of the image nodes in the candidate initial image files;

[0106] The node position of each image node in the candidate initial image file is represented by the processing batch of each image node when determining the redundant edges in the connected components;

[0107] C2. Determine a difference between a first merging threshold and a second merging threshold, and determine a product of the position coefficient and the difference;

[0108] C3. Using the difference between the first merging threshold and the product as the merging threshold for matching the two candidate initial image files.

[0109] Specifically, in the above embodiment, after determining the redundant edges based on the currently determined key original image nodes and adjacent image nodes, the key original image nodes and adjacent image nodes of the current batch will be marked as processed. At this time, the processing batch of the key original image nodes and adjacent image nodes can be determined, and the processing batch of each image node can be used as the node position.

[0110] Based on the node position of each image node in the candidate initial image file, the position coefficient between the candidate initial image files is determined. Specifically, the position coefficient can be expressed according to the degree of overlap of the node positions of each image node in the two candidate initial image files. For example, if there are four image nodes in candidate initial image file A, when redundant edges are determined for the connected component where the candidate initial image file is located, these four image nodes are processed at the 1st, 2nd, 2nd, and 5th times, respectively. There are five image nodes in candidate initial image file B, and when redundant edges are determined for the connected component where the candidate initial image file is located, these five image nodes are processed at the 2nd, 3rd, 4th, 5th, and 6th times, respectively. Among them, there are three image nodes in candidate initial image file A that have the same node position as two image nodes in candidate initial image file B, then the position coefficient can be expressed as (3+2) / (4+5)=55.6%.

[0111] After calculating the position coefficient, the merge threshold for matching the two candidate initial image files can be calculated using the following formula: ,in, represents the merge threshold for matching two candidate initial image archives, represents the first merging threshold, represents the second merging threshold, and k represents the position coefficient.

[0112] In this embodiment, it can be seen from the above formula that the higher the degree of node position overlap between image nodes in two candidate initial image files and the smaller the merging threshold set therefor, the easier it is for the two candidate initial image files to be merged into one image file.

[0113] In this embodiment, the dynamic setting of the image archive merging threshold within the connected component is achieved based on the node position overlap between image nodes in the initial image archive.

[0114] S250: If it is determined that the similarity between the candidate initial image files is greater than or equal to the merging threshold matching the two candidate initial image files, the two candidate initial image files are merged.

[0115] In this embodiment, if the similarity between two candidate initial image files is greater than or equal to the merging threshold dynamically set in the above embodiment, the two candidate initial image files may be merged, and the centroid of the merged image file may be recalculated.

[0116] It should be noted that the centroid of the merged image file also needs to be determined by weighting based on the missing degree of the target image node, which will not be further described in this embodiment.

[0117] S260. Re-determine the centroid of the merged image file, and re-execute the operation of determining the two candidate initial image files with the highest similarity in each initial image file and / or the merged image file, until the processing of all initial image files and / or the merged image files corresponding to the same connected component is completed, or the similarity of the candidate initial image files is less than the second merging threshold.

[0118] In this embodiment, the two candidate initial image files with higher similarity are prioritized for determining whether to merge, and the determination of whether to merge each two image files is performed in descending order of similarity, which can improve the efficiency of file merging.

[0119] S270 , performing secondary merging on the initial image files and / or the image files after the initial merging corresponding to different connected components according to a third merging threshold to obtain at least two target image files.

[0120] The third merging threshold is greater than the first merging threshold.

[0121] In this embodiment, after the files within all connected components are merged, a higher merging threshold is used to merge the image files between connected components.

[0122] In this embodiment, for image files with the same connected components, a lower threshold is used, and the threshold is dynamically determined using the degree of node position overlap as an aid, achieving accurate initial file merging. For image files with different connected components, a higher threshold is used for secondary merging. The merged image files become the final target image file.

[0123] The technical solution of this embodiment determines redundant edges within connected components based on the degree of image nodes and dynamically deletes them. It also records the missing degrees of image nodes at both ends of the redundant edges and evaluates the information integrity of the image nodes. After the clustering of the image is completed, the node weights are adjusted based on the node missing degrees to optimize the calculation of the image file centroid, effectively compensating for the impact of redundant edge deletion on the image file centroid characteristics. This allows the centroid calculated based on the adjusted node weights to more accurately restore the image clustering effect before the redundant edge deletion and accurately reflect the distribution characteristics of the original data within the initial image file. At the same time, a multi-level file merging method is designed. For image files within connected components, the merging threshold is dynamically adjusted based on the node position overlap, achieving accurate initial file merging. For image files between connected components, a higher threshold is used for secondary merging. This multi-level, dynamic threshold merging method further optimizes the image file merging effect and avoids the influence of the merging order on the file merging results. Using this embodiment for image clustering can meet the requirements for accurate clustering in large-scale complex network environments and improve clustering efficiency and accuracy.

[0124] Example 3

[0125] Figure 9 This is a structural diagram of an image aggregation device provided by the third embodiment of the present invention. Figure 9 As shown, the device includes:

[0126] The redundant edge determination module 310 is used to determine at least two connected components in the graph to be aggregated, and determine redundant edges in the connected components based on the degrees of the image nodes in the connected components;

[0127] An initial image archive determining module 320 is configured to perform image aggregation based on the to-be-aggregated image graph after redundant edges are deleted, to obtain at least two initial image archives;

[0128] The target image file determination module 330 is used to perform an initial merger of the initial image files corresponding to the same connected component, and perform a secondary merger of the initial image files corresponding to different connected components and / or the image files after the initial merger, to obtain at least two target image files.

[0129] The technical solution of the embodiment of the present invention determines the connected components of the graph to be clustered, determines the redundant edges in the connected components based on the degree of each image node in the connected components, deletes the redundant edges, and then clusters the graph to be clustered. After obtaining the initial image archive, the initial image archives within the same connected component are first merged, and then the image archives between different connected components are merged to obtain the final target image archive. This solves the problems of excessive memory consumption, low computing efficiency, and waste of computing power caused by large graph scales and the presence of redundant edges in the prior art. The embodiment of the present invention can reduce memory consumption and waste of computing resources, and improve the efficiency and accuracy of clustering.

[0130] Based on the above embodiment, optionally, the redundant edge determination module 310 includes:

[0131] a node determination unit, configured to determine key image nodes from among the image nodes of the connected components according to the degrees of the image nodes in the connected components, forming a key image node set, and to determine adjacent image nodes adjacent to the key image nodes, forming an adjacent image node set;

[0132] a candidate edge determination unit, configured to, if it is determined that the key image node and the adjacent image nodes form a loop, take the edges in the loop other than the edges between the key image nodes as candidate edges;

[0133] A redundant edge determination unit is used to determine the similarity between two image nodes corresponding to a candidate edge, and to use the candidate edge with the lowest similarity as a redundant edge;

[0134] The node culling unit is used to mark each image node in the key image node set and the adjacent image node set as processed, and repeatedly perform the operation of determining the key image nodes in the unprocessed image nodes of the connected component until all image nodes in the connected component are processed and all redundant edges in the connected component are obtained.

[0135] Based on the above embodiment, optionally, the node determination unit is specifically configured to:

[0136] Among the image nodes of the connected components, the image node with the highest degree of certainty is used as the key image node;

[0137] Determine the image node with the highest similarity among the image nodes connected to the key image node as the key image node, and repeat the process until a stop condition is determined to be satisfied;

[0138] The stopping condition includes that the number of key image nodes is greater than or equal to a preset node number threshold, and / or a loop is formed between the key image nodes;

[0139] The key image nodes are grouped into a key image node set.

[0140] Based on the above embodiment, optionally, the target image file determination module 330 includes:

[0141] a candidate initial image file determining unit, configured to determine the centroids of the initial image files corresponding to the same connected component, and to determine two candidate initial image files with the highest similarity among the initial image files based on the centroids of the initial image files;

[0142] a merging threshold determination unit, configured to determine a merging threshold that matches two candidate initial image files based on a preset first merging threshold and a second merging threshold;

[0143] an initial image file merging unit, configured to merge the two candidate initial image files if it is determined that the similarity between the candidate initial image files is greater than or equal to a merging threshold matching the two candidate initial image files;

[0144] a repetitive execution unit, configured to re-determine the centroid of the merged image file and re-execute the operation of determining two candidate initial image files with the highest similarity among the initial image files and / or the merged image files until all initial image files and / or the merged image files corresponding to the same connected component are processed, or the similarity of the candidate initial image files is less than a second merging threshold;

[0145] The first merging threshold is greater than the second merging threshold.

[0146] Based on the above embodiment, optionally, the candidate initial image file determining unit is specifically configured to:

[0147] For each initial image file corresponding to the same connected component, determine whether the target image node exists in the current initial image file;

[0148] The target image node is the image node corresponding to the redundant edge before deleting the redundant edge;

[0149] If it is determined that the target image node exists in the current initial image file, the node weight of the target image node is determined according to the number of redundant edges corresponding to the target image node;

[0150] According to the node weight of the target image node, the centroid of the current initial image file is determined.

[0151] Based on the above embodiment, optionally, a threshold determination unit is incorporated, specifically configured to:

[0152] Determining position coefficients between candidate initial image files according to the node positions of the image nodes in the candidate initial image files;

[0153] The node position of each image node in the candidate initial image file is represented by the processing batch of each image node when determining the redundant edges in the connected components;

[0154] determining a difference between a first merging threshold and a second merging threshold, and determining a product of the position coefficient and the difference;

[0155] The difference between the first merging threshold and the product is used as the merging threshold for matching the two candidate initial image files.

[0156] Based on the above embodiment, optionally, the target image file determination module 330 includes:

[0157] a secondary merging unit, configured to perform secondary merging on the initial image files corresponding to different connected components and / or the image files after the initial merging according to a third merging threshold;

[0158] The third merging threshold is greater than the first merging threshold.

[0159] The image aggregation device provided in the embodiment of the present invention can execute the image aggregation method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0160] Example 4

[0161] Figure 10 A schematic diagram of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0162] like Figure 10As shown, electronic device 10 includes at least one processor 11 and memory, such as read-only memory (ROM) 12 and random access memory (RAM) 13, communicatively connected to at least one processor 11. The memory stores computer programs executable by the at least one processor. Processor 11 can perform various appropriate actions and processes based on the computer programs stored in ROM 12 or loaded from storage unit 18 into RAM 13. RAM 13 can also store various programs and data required for the operation of electronic device 10. Processor 11, ROM 12, and RAM 13 are interconnected via bus 14. An input / output (I / O) interface 15 is also connected to bus 14.

[0163] Multiple components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0164] Processor 11 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any other suitable processor, controller, microcontroller, etc. Processor 11 executes the various methods and processes described above, such as the image aggregation method.

[0165] In some embodiments, the image archiving method can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the image archiving method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the image archiving method in any other suitable manner (e.g., via firmware).

[0166] Various embodiments of the systems and techniques described above can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0167] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0168] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device, or apparatus. A computer-readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or apparatus, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media may include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0169] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device that has: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0170] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0171] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0172] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0173] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. An image aggregation method, characterized in that: include: Determine at least two connected components in the graph to be aggregated, and determine redundant edges in the connected components according to the degrees of each image node in the connected components; Perform image clustering according to the to-be-clustered graph after deleting redundant edges, to obtain at least two initial image archives; Initial image files corresponding to the same connected component are initially merged, and initial image files corresponding to different connected components and / or image files after initial merging are secondary merged to obtain at least two target image files.

2. The method according to claim 1, characterized in that According to the degree of each image node in the connected component, the redundant edges in the connected component are determined, including: According to the degree of each image node in the connected component, key image nodes are determined in each image node of the connected component to form a key image node set, and adjacent image nodes adjacent to the key image nodes are determined to form an adjacent image node set; If it is determined that the key image node and the adjacent image nodes form a loop, then the edges in the loop except the edges between the key image nodes are taken as candidate edges; Determine the similarity between the two image nodes corresponding to the candidate edges, and take the candidate edge with the lowest similarity as a redundant edge; Each image node in the key image node set and the adjacent image node set is marked as processed, and the operation of determining the key image nodes in the unprocessed image nodes of the connected component is repeatedly performed until all image nodes in the connected component are processed and all redundant edges in the connected component are obtained.

3. The method according to claim 2, characterized in that According to the degree of each image node in the connected component, a key image node is determined in each image node of the connected component to form a key image node set, including: Among the image nodes of the connected components, the image node with the highest degree of certainty is used as the key image node; Among the image nodes connected to the key image node, determine the image node with the highest similarity as the key image node, and repeat the process until it is determined that a stop condition is met; The stopping condition includes that the number of key image nodes is greater than or equal to a preset node number threshold, and / or a loop is formed between the key image nodes; The key image nodes are grouped into a key image node set.

4. The method according to claim 1, characterized in that: The initial image files corresponding to the same connected component are initially merged, including: Determine the centroids of each initial image file corresponding to the same connected component, and determine two candidate initial image files with the highest similarity in each initial image file according to the centroids of each initial image file; Determining a merging threshold matching two candidate initial image archives according to a preset first merging threshold and a second merging threshold; If it is determined that the similarity between the candidate initial image files is greater than or equal to the merging threshold matching the two candidate initial image files, then the two candidate initial image files are merged; Re-determine the centroid of the merged image file, and re-execute the operation of determining the two candidate initial image files with the highest similarity in each initial image file and / or the merged image file, until the processing of all initial image files and / or the merged image files corresponding to the same connected component is completed, or the similarity of the candidate initial image files is less than the second merging threshold; The first merging threshold is greater than the second merging threshold.

5. The method according to claim 4, characterized in that Determine the centroid of each initial image file corresponding to the same connected component, including: For each initial image file corresponding to the same connected component, determine whether there is a target image node in the current initial image file; The target image node is the image node corresponding to the redundant edge before deleting the redundant edge; If it is determined that the target image node exists in the current initial image file, the node weight of the target image node is determined according to the number of redundant edges corresponding to the target image node; According to the node weight of the target image node, the centroid of the current initial image archive is determined.

6. The method according to claim 4, characterized in that Determining a merging threshold matching two candidate initial image archives according to a preset first merging threshold and a second merging threshold includes: Determining the position coefficients between the candidate initial image files according to the node positions of the image nodes in the candidate initial image files; Wherein, the node position of each image node in the candidate initial image archive is represented by the processing batch of each image node when determining the redundant edges in the connected component; Determine a difference between a first merging threshold and a second merging threshold, and determine a product of the position coefficient and the difference; The difference between the first merging threshold and the product is used as the merging threshold for matching the two candidate initial image files.

7. The method according to claim 4, characterized in that Performing secondary merging of initial image files corresponding to different connected components and / or image files after initial merging, including: Performing secondary merging on the initial image files and / or the image files after the initial merging corresponding to the different connected components according to the third merging threshold; The third merging threshold is greater than the first merging threshold.

8. An image aggregation device, characterized in that: include: A redundant edge determination module is used to determine at least two connected components in the to-be-aggregated graph, and determine redundant edges in the connected components according to the degrees of each image node in the connected components; An initial image archive determination module, used to perform image aggregation according to the to-be-aggregated image graph after redundant edges are deleted, to obtain at least two initial image archives; The target image file determination module is used to initially merge the initial image files corresponding to the same connected component, and to perform secondary merging on the initial image files corresponding to different connected components and / or the image files after the initial merging, so as to obtain at least two target image files.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the image archiving method as described in any one of claims 1-7 is implemented.

10. A storage medium storing computer executable instructions, characterized in that: When the computer executable instructions are executed by a computer processor, they are used to perform the image archiving method as described in any one of claims 1-7.

Images