464results about "Still image data querying" patented technology

The invention provides an image data and pattern spot management system and method and electronic equipment, and relates to the technical field of geographic information data processing, and the image data and pattern spot management system comprises an image data indexing module which is used for carrying out three-dimensional indexing on a remote sensing image to obtain a space-time-data source association chain of the remote sensing image, the remote sensing image contains pattern spot data with an identifier; the pattern spot complete cycle construction module is used for performing multi-temporal analysis on the pattern spot data to obtain an analysis result, and constructing a pattern spot change life cycle map of the pattern spot data in combination with the association chain; and the tracing module is used for positioning the target pattern spot when the target pattern spot needs to be traced to obtain a change node of the target pattern spot and a space-time-data source index corresponding to the change node, and searching in the association chain according to the change node and the space-time-data source index to obtain a traceable data chain. According to the invention, the storage and management of the geographic image data and the pattern spot data are more systematized and refined.

Systems and methods for image search result filtering can include obtaining a search query, determining a plurality of candidate image search results, processing the search query with a generative model to determine a plurality of search result criteria, and refining the plurality of candidate image search results based on determining whether the candidate results satisfy the plurality of search results criteria. The systems and methods can perform a plurality of determinations based on the output of the generative model.

The embodiment of the invention discloses a building engineering crack detection method and system based on image recognition, and the method comprises the steps: obtaining a building surface image, carrying out the preprocessing of the image, obtaining a standardized image, and carrying out the multi-scale decomposition extraction and integration of various features, and forming a multi-dimensional feature descriptor set; after feature importance is evaluated, a compact feature vector is generated through dimension reduction, quantization coding and compression, and then a multi-level feature index mechanism for optimized compression is constructed. A query feature vector is extracted from a newly collected image, searching and screening are completed by means of an index mechanism and a tolerance threshold, and a crack matching result is obtained; and based on the result, positioning cracks, classifying types, measuring parameters and evaluating severity, and generating a crack state report. The crack trend is analyzed in combination with the historical data time sequence, a multi-stage early warning mechanism is designed, maintenance suggestions are provided, and a real-time monitoring and early warning system is formed. According to the embodiment of the invention, the technical problems of high storage pressure and low real-time detection efficiency in the prior art can be effectively solved.

The invention belongs to the computer vision technology, and particularly relates to a small sample multi-modal prototype learning-oriented remote sensing scene classification method, which comprises the following steps of: acquiring RGB (Red, Green and Blue) images with category labels and text prompts of the RGB images as a support set; establishing a text prototype, an RGB prototype and a hyperspectral prototype of each category according to the support set; and extracting to-be-classified query set image features by using a pre-trained CLIP image encoder, calculating cosine similarities between the query set image features and the text prototype, the RGB prototype and the hyperspectral prototype of each category of the support set, taking the cosine similarities as input of a multi-layer perceptron, and obtaining the category of the to-be-classified RGB image through classification of the multi-layer perceptron. High-precision and high-robustness remote sensing scene classification is realized under the small sample condition, only prototype and similarity calculation is needed in the reasoning stage, and deployment and expansion are easy.

The invention discloses a multi-model reasoning method and device based on a graph structure, a medium and a program product, and the method comprises the steps: querying a task target node in a graph database, starting from the task target node, traversing the graph database according to a corresponding driving model, and carrying out reasoning path extension, each directed acyclic graph is obtained by mapping metadata of the registered analysis model; generating a task topology execution graph according to the model nodes involved in the expanded reasoning path and the corresponding driving model; and according to the task topology execution graph, actual execution of each model node is scheduled in sequence, and an interpretation result corresponding to the task target node is obtained. According to the method, the model attribution relation is explicitly analyzed, and the reasoning path is optimized by utilizing graph calculation, so that the reasoning execution efficiency is improved, and the intelligence, performance and interpretability of multi-model collaborative analysis are improved.

The invention relates to the technical field of channel monitoring and underwater topography analysis, in particular to a multi-beam sounding data driven underwater topography profile extraction method and device and a medium, and the method comprises the steps: obtaining channel region multi-beam sounding original data, carrying out the standardized preprocessing, and outputting a sounding data set; performing spatial interpolation and vectorization processing on the sounding data set to generate channel geological data, and issuing the channel geological data as a similar three-dimensional channel bottom graph through a geographic information service platform; generating a profile path, and generating interpolation points according to a preset interval; constructing a spatial index structure based on the sounding data set, and establishing an adjacent sounding point retrieval channel for each interpolation point; and in allusion to the same profile path, acquiring multiple periods of sounding data, calculating water depth difference, and dynamically and visually displaying an abnormal movement phenomenon by using a broken line graph. According to the method, efficient extraction, dynamic comparison and automatic early warning of the multi-period sounding data can be realized, and the analysis efficiency and accuracy of the channel topographic change are remarkably improved.

The invention relates to the technical field of computer vision processing, in particular to an automatic defect positioning method based on a BIM virtual image. The method comprises the following steps: establishing a high-rise building model, and making a data set integrating an illumination condition and a full view angle; calculating camera parameters; performing cross-modal image retrieval: performing targeted optimization on the basis of a classical ResNet architecture, and constructing a backbone network structure suitable for a building image retrieval task; initializing position attitude estimation based on matching; and correcting the camera position posture. According to the method, a defect fixed frame based on a building BIM virtual image is provided, a building image data set BIM-Vision based on Revit is constructed, rich visual angles and illumination condition setting are achieved, accurate camera position postures and 3D labels between beam columns are provided, high-quality basic data support is provided for building visual research, and the method has the advantages of being high in practicability and high in practicability. And during inspection, accurate positioning of defect positions and component association can be completed only by shooting a field image, so that the field operation process is greatly simplified.

The invention discloses a distributed medical image retrieval and hierarchical storage management system and method. The system comprises an interface processing module, a distributed image storage cluster, a routing forwarding module, a metadata management module and a storage management module. The interface processing module receives the image retrieval request, generates a routing identifier for positioning the storage position of a target medical image file and sends the routing identifier to the routing forwarding module; and the routing forwarding module queries a mapping relationship between the medical image file and the storage node and / or the storage hierarchy based on the routing identifier, determines a first storage node and forwards the request, so that the first storage node returns a target medical image file. And the storage management module obtains the access statistical information, determines a target storage level and / or a target storage node, migrates the medical image file, and triggers updating of the mapping relation after migration is completed. Therefore, cross-node accurate positioning and unified retrieval are realized, positioning consistency is kept after file migration, and hierarchical storage management based on access conditions is supported.

In an image processing method, a reference library and a query library are obtained; a reference image in the reference library and a prompt are inputted into a diffusion model to obtain estimated noise; the estimated noise is merged to obtain a reference noise feature; a plurality of query noise features corresponding to a query image are determined; and a target label corresponding to the query image is determined based on feature similarities between the plurality of query noise features and the reference noise features.

To provide an information processing device which acquires a screenshot posted by a user, so as to offer complementary information for complementing a content of the screenshot to viewers of the screenshot.SOLUTION: An information processing device includes an acquisition section, a generation section, and a provision section. The acquisition section acquires a screenshot posted by a user. The generation section generates complementary information for complementing a content of the screenshot on the basis of information on the screenshot and attribute information on viewers of the screenshot. The provision section provides the complementary information together with the screenshot to the viewers.SELECTED DRAWING: Figure 1

This document describes techniques and systems that enable privacy controls for sharing embeddings for searching and indexing media content. A set of images of a user's face are obtained and a machine-learned model is applied to the set of images to generate a user-specific dataset of face embeddings for the user. Media content stored in a media storage is indexed by applying the machine-learned model to the media content to provide indexed media information identifying one or more faces shown in the media content. Access to the indexed media information by another user querying the media content for images or videos depicting the user is controlled based on a digital key shared by the user with the other user, where the digital key is associated with the user-specific dataset and the user-specific dataset is usable to identify the images or videos depicting the user.

The invention discloses a privacy protection unsupervised cross-domain image retrieval method and system, and belongs to the technical field of information security. Aiming at the problems of privacy leakage risk and difficult feature alignment in the existing unsupervised cross-domain image retrieval, the invention provides a dual-protection transformation strategy, and realizes local encryption of image features of each data domain by combining orthogonal projection transformation and a differential privacy mechanism. Furthermore, intra-domain discriminant reinforcement learning and differential privacy cross-domain alignment are respectively performed through a probability prototype optimal transmission mechanism, and an intra-domain and cross-domain loss function is constructed based on a contrast learning normal form, so that safe and effective model training is realized. And after training is completed, image similarity calculation is performed by adopting protected feature representation, so that cross-domain image retrieval under privacy protection is realized. According to the method, the accuracy and robustness of cross-domain retrieval are improved, privacy security and model performance are considered, and the method is suitable for an image retrieval task in a multi-party cooperation scene.

The invention provides a visual question and answer method based on a domain self-adaptive retrieval decision, which comprises the following steps of: forming an input triple (x, q, d) comprising an image, a question text and an image description text; feature modal extraction and domain identification; generating an explicit reasoning track and a preliminary answer by using a chain reasoning technology CoT; according to a preset decision rule, judging that the preliminary answer is output as a final answer or enters the next step; based on the input triad (x, q, d) and the reasoning track, image retrieval and text retrieval are executed, and an enhanced knowledge set is generated; using the enhanced knowledge set to generate a final answer through a chain reasoning technology CoT, performing credibility verification, and outputting the final answer or a preset unknown identifier according to a verification result; according to the method, through reasoning-driven adaptive retrieval and multi-modal knowledge reordering, efficient utilization and real-time supplement of external knowledge are realized, and the accuracy and robustness of visual questions and answers are effectively improved.

A natural language image searching method is applied to an image searching system including an image analysis unit, a data management unit and an instruction input unit. The natural language image searching method includes an image feature vector encoder of the image analysis unit receiving a detection image and generating an image feature vector, the image analysis unit transmitting the detection image, the image feature vector, and a time stamp associated with the detection image to the data management unit, a text encoder of the instruction input unit generating and transmitting a text feature vector to the data management unit in accordance with a query statement, and the data management unit determining whether the query statement generated by the instruction input unit via natural language conforms to the detection image in accordance with a comparison result between the text feature vector and the image feature vector.

The invention relates to the field of information retrieval, and provides a user gallery multi-modal retrieval method and system based on AI intention recognition. The method comprises the following steps: performing intention analysis on a query statement input by a user through a multi-modal semantic understanding model to obtain an intention classification result and structured query representation; according to the intention classification result, distributing a retrieval weight of multi-source gallery data, and obtaining a weight configuration scheme for the current query; inputting the structured query representation and the weight configuration scheme into a hybrid retrieval engine, and performing collaborative retrieval processing on image data, text data and metadata in a user image library to obtain a candidate retrieval result set; and according to a multi-modal similarity calculation method, performing comprehensive scoring on a plurality of items in the candidate retrieval result set to obtain a sorted final retrieval result. According to the method, the complex query intention can be accurately identified, the retrieval weight is dynamically adjusted, and accurate multi-modal collaborative retrieval is realized on the premise of protecting privacy.

A method for managing a framework includes: receiving, by a GUI, a query that is sent to a first module; analyzing, by the first module, the query to infer intention; making, by the first module, a determination that the intention is not searching for an object in a database; sending, by the first module, the query to a second module; transforming, by the second module, the query into a vector that is sent to a third module; performing, by the third module and using the vector, a search for a nearest image in the database; identifying, by the third module, a path associated with the nearest image, which is sent to an analyzer, in which the analyzer further sends the path to a fourth module; and fetching, by the fourth module and based on the path, an image from a store, which is sent to the GUI.

The invention relates to the technical field of artificial intelligence, and discloses a cross-domain image semantic retrieval method and system based on multi-modal feature fusion, and the method comprises the steps: obtaining a query image and associated text description; wherein the query image is an image uploaded by a user, and the associated text description is a semantic word used for describing the query image; performing cross-modal feature fusion on the query image and the associated text description to determine a fused query feature; searching a target image from a target image library based on the fused query features; and taking the target image as a cross-domain image semantic retrieval result. According to the image semantic retrieval method and device, the query image is retrieved in combination with the multi-modal features, semantic information is enriched through associated text description, and the accuracy of image semantic retrieval is improved.

A data processing system implements an image generation system configured to operate in a first generation mode providing requested image contents based on prestored image assets without using an AI model to generate the requested image contents, a second generation mode generating the requested image contents using the AI model; and a hybrid mode, receiving a first textual prompt first image content; analyzing the first textual prompt, evaluating whether the image generation system includes prestored image content in the image asset repository that satisfies the first textual prompt, and responsive to determining that the image generation system lacks at least one of prestored image content in the image asset repository that satisfies at least one image element of the first image content requested by the first textual prompt, operating the image generation system in the hybrid generation mode to generate the first image content.

The invention relates to the technical field of query methods, in particular to a mobile query method and system for planning natural resources, and the method comprises the following steps: loading a three-dimensional virtual earth model, and obtaining three-dimensional space position data of the natural resources; and based on progressive processing of high-dimensional position transformation, wavelet dimensionality reduction, non-linear heterogeneous mapping, multi-resolution optimization and dynamic feedback control, multi-level visual display of the natural resources under different resolutions is obtained so as to realize spatial position accurate mapping and dynamic real-time display of the natural resources, and through a high-dimensional position transformation algorithm, the spatial position of the natural resources can be accurately mapped and dynamically displayed in real time. Three-dimensional coordinate data of natural resources are mapped to a high-dimensional spherical coordinate space, and high-dimensional data are compressed to core low-dimensional features through a multi-scale wavelet dimension reduction algorithm, so that data simplification is realized on the basis of retaining key information; the heterogeneous mapping algorithm realizes smooth conversion from low-dimensional features to two-dimensional display, and a Laplace-Beltrami operator is utilized to ensure the smoothness of the display.

An artificial intelligence (AI) system is provided that estimates the inferior vena cava (IVC) collapse rate and right atrial pressure (RAP) in real time from echocardiogram examinations. The system includes an image retrieval network configured to receive images from echocardiogram examinations and perform a quality assessment of the images to generate selected images. The system further includes a region segmentation network configured to localize and segment the selected images to obtain an IVC region in the selected images. The system further includes an IVC quantification and RAP estimation network that performs distance calculations to determine the diameters of the IVC region at different spatial and temporal points, enabling a more reliable estimation of the RAP value; and the system further includes an open-world active learning system having a classification engine and a clustering engine.

The application provides a zero-shot multi-modal information retrieval method and system based on fine-grained text inversion, obtains a reference image and modified text; a pre-trained fine-grained text inversion network is used to map the reference image into subject-oriented pseudo word labels and attribute-oriented pseudo word labels, and the modified text is spliced to obtain a combined query in the form of text; the cosine similarity between the representation vector of the combined query and the target image vector is used as the basis for retrieval to retrieve the corresponding picture. The application obtains subject-oriented pseudo word labels and attribute-oriented pseudo word labels based on the global features and local features of the image respectively, and uses different title-based semantic regular constraints to regulate the pseudo word labels, so that more efficient and accurate retrieval results are achieved.

The application is suitable for the technical field of data processing, and provides a method and device for image processing, electronic equipment and storage medium, the method comprising: in response to a first operation initiated by a user, acquiring multiple images; for any image in the multiple images, determining an image order of the image in the multiple images according to a feature parameter of the image in at least one visual feature dimension; and generating an album based on the image order of the multiple images, wherein each image in the album is sorted based on the image order. In the embodiment of the application, the order of the images in the album is determined according to the difference degree between the visual feature dimensions, the similarity of the image content between adjacent images is improved, the continuity of switching between different images in the album by the user is improved, the fragmentation of the picture is reduced, and the use experience of the user is improved.

The present disclosure relates to a system, method and non-transitory computer readable medium for reverse image searching. The system includes a storage device storing a set of instructions; and one or more processors in communication with the storage device. When executing the set of instructions, the one or more processors: obtain a target part of reference image features of a reference image; obtain a target part of target image features of a target image; determine, based on the target part of the reference image features and the target part of the target image features, whether the target image is similar to the reference image; and mark, upon a determination that the target image is similar to the reference image, the target image as a similar image of the reference image.

The present disclosure provides an image retrieval method, a model training method, an apparatus, a device, a medium and a product, relates to the field of image processing, and in particular to the field of deep learning. The specific implementation scheme is: obtaining a to-be-retrieved image; extracting visual features of the to-be-retrieved image based on a visual retrieval model obtained through joint training of multiple training tasks; extracting semantic features of the to-be-retrieved image based on a semantic retrieval model obtained through joint training of multiple loss functions; performing feature splicing on the visual features and the semantic features to obtain target features of the to-be-retrieved image; and searching for a target image that meets an image similarity condition with the to-be-retrieved image from an image library based on the target features. The technical scheme of the present disclosure improves the image retrieval accuracy.

To provide an information processing apparatus, an information processing method, and an information processing program capable of improving the convenience for a user.SOLUTION: An information processing apparatus includes a receiving unit, a specifying unit, and an acquisition unit. The receiving unit receives one or more keywords designated by a user. The specifying unit specifies a hair image based on the designated one or more keywords received by the receiving unit, using a learning model which receives the one or more keywords and outputs the hair image. The acquisition unit acquires similar hair images which are images including a hair image that is similar to the hair image specified by the specifying unit.SELECTED DRAWING: Figure 8

The application discloses a node positioning method and device, and relates to the technical field of computers. A specific embodiment of the method comprises the following steps: acquiring a relationship node identifier set of each node according to edge data in graph data, and determining an initial root node in a preset mode; for the nodes in the relationship node identifier set of the initial root node, if the nodes are leaf nodes, the leaf nodes are separately grouped, and if the nodes are non-leaf nodes, the nodes are grouped according to connection characteristics; the order of the leaf node groups and the non-leaf node groups is determined to determine a new relationship node sequence; and the position information of each leaf node and each non-leaf node is calculated according to the new relationship node sequence and taking the position of the initial root node as a starting point. The embodiment takes the relationship node identifier set of the node as a calculation basis, and can quickly obtain a simple, ordered and efficient mesh graph layout calculation result without setting an initial configuration and without multiple tests, compared with an existing force-directed algorithm.

The application discloses a method and system for optimizing a textile dyeing formula, and optimizes a computer theoretical formula by establishing a smart matching point, and improves a one-time matching success rate.The technical scheme is as follows: by establishing a mathematical model, on the basis of realizing computer system fast matching, by taking actual dyeing results in a laboratory or on a production line as the smart matching point, the relationship between the theoretical formula and the actual formula is optimized, and the one-time matching success rate is improved.Furthermore, in combination with an actual sample printing environment, repeated color correction caused by differences in different dyes, dyeing processes and the like can be effectively avoided, and the color matching efficiency and the color matching success rate are obviously improved.

An online trading system for identifying counterfeit goods is provided. The sale of counterfeit items has always been a problem in marketplaces, especially in auction houses. When sellers submit items for sale in marketplaces, there is a need for automated detection of counterfeit items. The disclosed technology receives information about the items to be traded. Information associated with the item includes metadata related to the item and an image of the item. The method uses a database of items known not to be sold (e.g., stock photos) or counterfeit items. The method matches the item's data with data in the database and identifies the item as counterfeit based on the matching result. The matching operation includes data analysis (e.g., image analysis using features of image data). Using the disclosed technology, counterfeit items can be detected automatically and efficiently when sellers submit items for sale in marketplaces, thereby improving the reliability of marketplaces from the buyer's perspective.