Image recognition management system and method based on data analysis

Abstract

The invention discloses an image recognition management system and method based on data analysis, and relates to the technical field of image recognition. The system comprises an initial classification module, a labeling information analysis and storage module, an effective feedback index analysis module, an optimal labeling algorithm determination module and a real-time target image storage reminding module. The initial classification module is used for performing initial classification of target images on any image data set comprising a plurality of images; the annotation information analysis and storage module is used for storing the associated image into a database corresponding to the identification keyword based on the annotation information; the effective feedback index analysis module is used for analyzing an effective feedback index of each type of target image corresponding to each type of labeling algorithm; the optimal annotation algorithm determination module is used for determining the optimal annotation algorithm of each category of target images, and the real-time target image storage reminding module is used for analyzing the optimal image category of the real-time target images and carrying out annotation algorithm storage reminding according to the optimal annotation algorithm corresponding to the optimal image category.

Description

Technical Field

[0001] The present invention relates to the technical field of image recognition management, and in particular to an image recognition management system and method based on data analysis. Background Art

[0002] Early image management was mainly based on a simple database system, which stored images in the file system and recorded the image file name, storage path, and basic annotation information (such as shooting time, shooting location, etc.) through the database. This management method can meet basic needs when the number of images is small and the application scenario is simple, but the efficiency of retrieval and utilization of image content is low; with the development of image recognition technology, image recognition systems came into being. It allows users to search by image content features (such as color, texture, shape, etc.). For example, a user can upload a landscape image containing a blue sky and green grass. The system extracts features such as the color histogram of the image, searches for images with similar color features in the database, and returns them to the user; however, this requires the use of a large number of identification algorithms to process the image data, and different identification algorithms may have different identification results for the same type of image. When applied to actual image databases, the user's database-based search results also have huge differences in the face of different identification results. Therefore, it is worth studying how to effectively guide the identification algorithm of various types of images to a certain extent to achieve efficient user retrieval. Summary of the invention

[0003] The object of the present invention is to provide an image recognition management system and method based on data analysis to solve the problems raised in the prior art.

[0004] To achieve the above object, the present invention provides the following technical solution: an image recognition management method based on data analysis, characterized in that the management method comprises the following specific steps:

[0005] S1: Initial classification of target images for any image dataset consisting of several images;

[0006] The purpose of initial classification is to facilitate image structure analysis after the image annotation information is generated by the image identification method, as well as the rapid correspondence between similarities between images, which can effectively solve the efficient classification of massive image data;

[0007] S2: Based on the classified target images, different types of image recognition and annotation algorithms are executed on all target images in each category, and annotation information of the corresponding associated images generated after the annotation is completed is recorded, and the associated images are stored in a database corresponding to the identification keywords based on the annotation information;

[0008] S3: Apply the database to the user side, extract the user's image retrieval events based on the database during the monitoring period, and obtain the retrieval factors and retrieval results recorded in the image retrieval event, corresponding to the image category in the image retrieval event, and analyze the effective feedback index of each category of target image corresponding to each type of annotation algorithm;

[0009] S4: Based on the effective feedback index, determine the best annotation algorithm for each category of target images, analyze the best image category for the real-time target image, and use the best annotation algorithm corresponding to the best image category to store the annotation algorithm reminder.

[0010] Furthermore, the initial classification of the target image for any image dataset consisting of several images includes the following specific steps:

[0011] The target image refers to the image corresponding to the number of identifiable scene elements in the image data that is greater than or equal to n. The purpose of screening out the target image is to avoid the error caused by the single scene of the image itself on the data set analysis. In addition, the target image of a single scene element is relatively simple in the application of the annotation algorithm and does not bring about a difference in retrieval efficiency for user retrieval.

[0012] Implement a labeling algorithm that can automatically generate labeling information based on the image content on the target image, and obtain identification keywords output by the labeling algorithm about the target image; there are at least two types of labeling algorithms; the identification keywords refer to the elements labeled after the image content is identified in the labeling algorithm; each target image corresponds to the identification keywords in all the labeling algorithms to form labeling information;

[0013] Select the annotation information containing the most identification keywords as the effective annotation information of the corresponding target image; the purpose of implementing different annotation algorithms for each target image is to improve the accuracy of identifying the target image, and take the annotation information that can better summarize the image content as the effective annotation information; traverse each target image in the image data set to obtain the corresponding effective annotation information;

[0014] Sort each target image in the image data set in ascending order according to the total number of identification keywords in the valid annotation information, and preliminarily divide the target images containing the same total number of identification keywords into the same category of target images and mark them as the first category of target images;

[0015] The average similarity of the identification keywords recorded by any two target images in each first-category target image is calculated by traversing, and the average similarity refers to the average similarity value obtained by performing a one-to-one matching analysis on all the identification keywords recorded by the two target images; all target images corresponding to the average similarity difference in each first-category target image that is less than or equal to the difference threshold are marked as key target images; all first-category target images are traversed until the division is completed to generate key target images of various categories.

[0016] Furthermore, storing the associated images in a database corresponding to the identified keywords based on the annotation information includes the following specific steps:

[0017] Extract identification keywords from the annotation information of each type of key target image record, and combine the identification keywords in any number to generate keyword pairs under all conditions; the keyword pairs contain at least two identification keywords;

[0018] The key target image is then stored in the database where the keyword pair under each condition is located, so that when the user searches with the keyword pair, the stored key target image is extracted based on the marked keyword pair in the database.

[0019] Furthermore, step S3 includes the following specific steps:

[0020] Step S31: An image retrieval event refers to an event in which a user inputs keyword content related to an image based on a database and selects a target image; a retrieval factor refers to the keyword content recorded in the search box, and a retrieval result refers to a user retrieving the desired target image based on the database and recording the result as a successful retrieval; when the user processes the target image and the keyword content in the search box is unidirectional, the corresponding target image is output as the desired target image;

[0021] Unidirectionality means that the changes in keyword content must satisfy the unique correspondence between keyword pairs in the annotation information;

[0022] Step S32: extract the retrieval factors that record all key target images in the image retrieval event, match the retrieval factors to the annotation algorithm corresponding to the annotation information, obtain the number of image retrieval events U1 that record successful retrieval under each annotation algorithm and the total number of image retrieval events U2 that apply the annotation algorithm to execute; define the time period when all types of key target images are recorded in the image retrieval event as the effective period; use the formula:

[0023] Z=k1×(U1 / U2)+k2×(R2 / R1);

[0024] Calculate the effective feedback index Z of each type of annotation algorithm for each type of key target image; R1 represents the number of keywords contained in the keyword pair with the highest success rate under the same algorithm, R2 represents the maximum number of keywords contained in the keyword pair generated under the same algorithm, and the success rate refers to the ratio of the number of successful retrieval events to the total number of image retrieval events with the same keyword pair; k1 and k2 represent the corresponding reference coefficients.

[0025] The larger the Z is, the higher the achievement rate and efficiency of this type of annotation algorithm in realizing user-based database image retrieval.

[0026] Further, step S4 includes the following:

[0027] Sort the various annotation algorithms according to the effective feedback index Z value from large to small, and select the annotation algorithm ranked first as the best annotation algorithm for the target image of the corresponding category;

[0028] Analyze the best image categories for real-time target images, including the following:

[0029] Determine effective annotation information for the real-time target image; extract the number of identification keywords recorded in the effective annotation information, and match the target image with the same number of real-time identification keywords to form a first image set;

[0030] Based on the first image set, the keyword similarity between each target image in the first image set and the real-time target image is calculated, and the target image corresponding to the maximum keyword similarity is selected as the best image category.

[0031] An image recognition management system based on data analysis, the management system includes an initial classification module, a labeling information analysis and storage module, an effective feedback index analysis module, an optimal labeling algorithm determination module and a real-time target image storage reminder module;

[0032] The initial classification module is used to perform initial classification of target images for any image data set consisting of several images;

[0033] The annotation information analysis and storage module is used to store the associated images into a database corresponding to the identification keywords based on the annotation information;

[0034] The effective feedback index analysis module is used to analyze the effective feedback index of each category of target images corresponding to various types of annotation algorithms;

[0035] The optimal labeling algorithm determination module is used to determine the optimal labeling algorithm for each category of target images.

[0036] The real-time target image storage reminder module is used to analyze the best image category of the real-time target image and to perform storage reminder of the annotation algorithm using the best annotation algorithm corresponding to the best image category.

[0037] Further, the initial classification module includes a labeling algorithm implementation unit, a labeling keyword generation unit, a valid labeling information analysis unit, a first-category target image marking unit, and a key target image division unit;

[0038] The annotation algorithm implementation unit is used to implement an annotation algorithm for the target image that can automatically generate annotation information based on the image content.

[0039] The identification keyword generating unit is used to obtain identification keywords output by the annotation algorithm regarding the target image;

[0040] The effective annotation information analysis unit is used to form annotation information corresponding to each target image by identifying keywords in all annotation algorithms; the annotation information containing the most identifying keywords is selected as the effective annotation information corresponding to the target image;

[0041] The first-category target image marking unit is used to preliminarily classify the target images containing the same total number of identification keywords into the same category of target images and mark them as the first category of target images;

[0042] The key target image division unit is used to mark all target images corresponding to the average similarity difference value less than or equal to the difference threshold value in each first-category target image as key target images.

[0043] Further, the effective feedback index analysis module includes a retrieval success event analysis unit and an effective feedback index calculation unit;

[0044] The retrieval success event analysis unit is used to extract the event that the user retrieves the required target image based on the database and records it as a retrieval success;

[0045] The effective feedback index calculation unit is used to calculate the effective feedback index of each type of key target image corresponding to each type of labeling algorithm.

[0046] Furthermore, the real-time target image storage reminder module includes a keyword number matching unit, a similarity analysis unit and an optimal image category output unit;

[0047] The keyword number matching unit is used to determine the effective annotation information for the real-time target image; extract the number of identification keywords recorded in the effective annotation information, and match the target image with the same number of real-time identification keywords to form a first image set;

[0048] The similarity analysis unit is used to calculate the keyword similarity between each target image in the first image set and the real-time target image;

[0049] The best image category output unit is used to select the target image corresponding to the maximum keyword similarity value as the best image category.

[0050] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention identifies key target images that can be used as analysis objects by screening and analyzing images in an image database, and uses a multi-category identification algorithm to screen out identification keywords that maximize the representation of image features based on the key target images, and stores the images accordingly, so as to achieve effective extraction when users search based on the image database. At the same time, the user's search events based on the database are analyzed to determine the best identification algorithm for each type of key target image. From the perspective of the user's retrieval efficiency, the data has a strong theoretical foundation and high data accuracy, thereby solving the problem of effectively giving the best identification algorithm for the corresponding image when new image data is input into the image library, so as to achieve efficient retrieval by users. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 The present invention is a structural schematic diagram of an image recognition management system based on data analysis. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments 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 creative work are within the scope of protection of the present invention.

[0053] Example: Figure 1 As shown, the present invention provides an image recognition management system and method technical solution based on data analysis, an image recognition management method based on data analysis, characterized in that the management method includes the following specific steps:

[0054] S1: Initial classification of target images for any image dataset consisting of several images;

[0055] The purpose of initial classification is to facilitate image structure analysis after the image annotation information is generated by the image identification method, as well as the rapid correspondence between similarities between images, which can effectively solve the efficient classification of massive image data;

[0056] S2: Based on the classified target images, different types of image recognition and annotation algorithms are executed on all target images in each category, and annotation information of the corresponding associated images generated after the annotation is completed is recorded, and the associated images are stored in a database corresponding to the identification keywords based on the annotation information;

[0057] S3: Apply the database to the user side, extract the user's image retrieval events based on the database during the monitoring period, and obtain the retrieval factors and retrieval results recorded in the image retrieval event, corresponding to the image category in the image retrieval event, and analyze the effective feedback index of each category of target image corresponding to each type of annotation algorithm;

[0058] S4: Based on the effective feedback index, determine the best annotation algorithm for each category of target images, analyze the best image category for the real-time target image, and use the best annotation algorithm corresponding to the best image category to store the annotation algorithm reminder.

[0059] The initial classification of the target image for any image dataset consisting of several images includes the following specific steps:

[0060] The target image refers to the image corresponding to the number of identifiable scene elements in the image data that is greater than or equal to n. Generally, n=3 is set. The purpose of screening out the target image is to avoid the error caused by the single scene of the image itself on the data set analysis. The target image of a single scene element is relatively simple in the application of the annotation algorithm and does not bring about a difference in retrieval efficiency for user retrieval.

[0061] Implement a labeling algorithm that can automatically generate labeling information based on the image content on the target image, and obtain identification keywords output by the labeling algorithm about the target image; there are at least two types of labeling algorithms; the identification keywords refer to the elements labeled after the image content is identified in the labeling algorithm; each target image corresponds to the identification keywords in all the labeling algorithms to form labeling information;

[0062] The annotation algorithms in this application may be target detection and recognition technology, scene understanding and semantic segmentation, and image feature extraction and label generation; each type of algorithm can generate annotation information corresponding to the target image;

[0063] Select the annotation information containing the most identification keywords as the effective annotation information of the corresponding target image; the purpose of implementing different annotation algorithms for each target image is to improve the accuracy of identifying the target image, and take the annotation information that can better summarize the image content as the effective annotation information; traverse each target image in the image data set to obtain the corresponding effective annotation information;

[0064] Sort each target image in the image data set in ascending order according to the total number of identification keywords in the valid annotation information, and preliminarily divide the target images containing the same total number of identification keywords into the same category of target images and mark them as the first category of target images;

[0065] The average similarity of the identification keywords recorded by any two target images in each first-category target image is calculated by traversing, and the average similarity refers to the average similarity value obtained by performing a one-to-one matching analysis on all the identification keywords recorded by the two target images; all target images corresponding to the average similarity difference in each first-category target image that is less than or equal to the difference threshold are marked as key target images; all first-category target images are traversed until the division is completed to generate key target images of various categories.

[0066] Storing the associated images in a database corresponding to the identified keywords based on the annotation information includes the following specific steps:

[0067] Extract identification keywords from the annotation information of each type of key target image record, and combine the identification keywords in any number to generate keyword pairs under all conditions; the keyword pairs contain at least two identification keywords;

[0068] The key target image is then stored in the database where the keyword pair under each condition is located, so that when the user searches with the keyword pair, the stored key target image is extracted based on the marked keyword pair in the database.

[0069] As shown in the embodiment: there is a key target image 1, and the algorithms for image recognition and labeling of the key target image 1 are algorithm a and algorithm b;

[0070] Algorithm a generates annotation information based on key target image 1: keyword 1 → keyword 2 → keyword 3; then any number of combinations are used to generate keyword pairs under all conditions, including the following:

[0071] Keyword 1 → Keyword 2, Keyword 1 → Keyword 3, Keyword 2 → Keyword 3, Keyword 1 → Keyword 2 → Keyword 3;

[0072] Then the key target image 1 is stored in the keyword pair marked as "keyword 1→keyword 2" in the database; similarly, the other three keyword pairs will also store the key target image 1, but the retrieval method is different.

[0073] Step S3 includes the following specific steps:

[0074] Step S31: The image retrieval event refers to the event in which the user inputs keyword content related to the image based on the database and selects the target image; the retrieval factor refers to the keyword content recorded in the search box, and the retrieval result refers to the user retrieving the required target image based on the database and recording it as a successful retrieval; when the user performs a processing operation on the target image and the keyword content in the search box is unidirectional, the corresponding target image is output as the required target image; the processing operation includes copying, downloading, etc.;

[0075] Unidirectionality means that the changes in keyword content must satisfy the unique correspondence between keyword pairs in the annotation information;

[0076] Step S32: extract the retrieval factors that record all key target images in the image retrieval event, match the retrieval factors to the annotation algorithm corresponding to the annotation information, obtain the number of image retrieval events U1 that record successful retrieval under each annotation algorithm and the total number of image retrieval events U2 that apply the annotation algorithm to execute; define the time period when all types of key target images are recorded in the image retrieval event as the effective period; use the formula:

[0077] Z=k1×(U1 / U2)+k2×(R2 / R1);

[0078] Calculate the effective feedback index Z of each type of annotation algorithm for each type of key target image; R1 represents the number of keywords contained in the keyword pair with the highest success rate under the same algorithm, R2 represents the maximum number of keywords contained in the keyword pair generated under the same algorithm, and the success rate refers to the ratio of the number of successful retrieval events to the total number of image retrieval events with the same keyword pair; k1 and k2 represent the corresponding reference coefficients.

[0079] The larger the Z is, the higher the achievement rate and efficiency of this type of annotation algorithm in realizing user-based database image retrieval.

[0080] Step S4 includes the following:

[0081] Sort the various annotation algorithms according to the effective feedback index Z value from large to small, and select the annotation algorithm ranked first as the best annotation algorithm for the target image of the corresponding category;

[0082] Analyze the best image categories for real-time target images, including the following:

[0083] Determine effective annotation information for the real-time target image; extract the number of identification keywords recorded in the effective annotation information, and match the target image with the same number of real-time identification keywords to form a first image set;

[0084] Based on the first image set, the keyword similarity between each target image in the first image set and the real-time target image is calculated, and the target image corresponding to the maximum keyword similarity is selected as the best image category.

[0085] An image recognition management system based on data analysis, the management system includes an initial classification module, a labeling information analysis and storage module, an effective feedback index analysis module, an optimal labeling algorithm determination module and a real-time target image storage reminder module;

[0086] The initial classification module is used to perform initial classification of target images for any image data set consisting of several images;

[0087] The annotation information analysis and storage module is used to store the associated images into a database corresponding to the identification keywords based on the annotation information;

[0088] The effective feedback index analysis module is used to analyze the effective feedback index of each category of target images corresponding to various types of annotation algorithms;

[0089] The optimal labeling algorithm determination module is used to determine the optimal labeling algorithm for each category of target images.

[0090] The real-time target image storage reminder module is used to analyze the best image category of the real-time target image and to perform storage reminder of the annotation algorithm using the best annotation algorithm corresponding to the best image category.

[0091] The initial classification module includes a labeling algorithm implementation unit, a labeling keyword generation unit, a valid labeling information analysis unit, a first-category target image marking unit, and a key target image division unit;

[0092] The annotation algorithm implementation unit is used to implement an annotation algorithm for the target image that can automatically generate annotation information based on the image content.

[0093] The identification keyword generating unit is used to obtain identification keywords output by the annotation algorithm regarding the target image;

[0094] The effective annotation information analysis unit is used to form annotation information corresponding to each target image by identifying keywords in all annotation algorithms; the annotation information containing the most identifying keywords is selected as the effective annotation information corresponding to the target image;

[0095] The first-category target image marking unit is used to preliminarily classify the target images containing the same total number of identification keywords into the same category of target images and mark them as the first category of target images;

[0096] The key target image division unit is used to mark all target images corresponding to the average similarity difference value less than or equal to the difference threshold value in each first-category target image as key target images.

[0097] The effective feedback index analysis module includes a retrieval success event analysis unit and an effective feedback index calculation unit;

[0098] The retrieval success event analysis unit is used to extract the event that the user retrieves the required target image based on the database and records it as a retrieval success;

[0099] The effective feedback index calculation unit is used to calculate the effective feedback index of each type of key target image corresponding to each type of labeling algorithm.

[0100] The real-time target image storage reminder module includes a keyword number matching unit, a similarity analysis unit and an optimal image category output unit;

[0101] The keyword number matching unit is used to determine the effective annotation information for the real-time target image; extract the number of identification keywords recorded in the effective annotation information, and match the target image with the same number of real-time identification keywords to form a first image set;

[0102] The similarity analysis unit is used to calculate the keyword similarity between each target image in the first image set and the real-time target image;

[0103] The best image category output unit is used to select the target image corresponding to the maximum keyword similarity value as the best image category.

[0104] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. An image recognition management method based on data analysis, characterized in that: The management method comprises the following specific steps: S1: Initial classification of target images for any image dataset consisting of several images; S2: Based on the classified target images, different types of image recognition and annotation algorithms are executed on all target images in each category, and annotation information of the corresponding associated images generated after the annotation is completed is recorded, and the associated images are stored in a database corresponding to the identification keywords based on the annotation information; S3: Apply the database to the user side, extract the user's image retrieval events based on the database during the monitoring period, and obtain the retrieval factors and retrieval results recorded in the image retrieval event, corresponding to the image category in the image retrieval event, and analyze the effective feedback index of each category of target image corresponding to each type of annotation algorithm; S4: Based on the effective feedback index, determine the best annotation algorithm for each category of target images, analyze the best image category for the real-time target image, and use the best annotation algorithm corresponding to the best image category to store the annotation algorithm reminder.

2. The image recognition management method based on data analysis according to claim 1, characterized in that: The initial classification of the target image for any image data set consisting of a plurality of images comprises the following specific steps: The target image refers to the image corresponding to the case where the number of identifiable scene elements contained in the image data is greater than or equal to n; Implementing a labeling algorithm that can automatically generate labeling information based on the image content on the target image, and obtaining identification keywords output by the labeling algorithm about the target image; the labeling algorithm is of at least two types; the identification keywords refer to the elements labeled after the image content is identified in the labeling algorithm; each target image is corresponding to the identification keywords in all the labeling algorithms to form labeling information; Select the annotation information containing the most identification keywords as the valid annotation information of the corresponding target image; traverse each target image in the image data set to obtain the corresponding valid annotation information; Sort each target image in the image data set in ascending order according to the total number of identification keywords in the valid annotation information, and preliminarily divide the target images containing the same total number of identification keywords into the same category of target images and mark them as the first category of target images; Traversing and calculating the average similarity of identification keywords recorded by any two target images in each first-category target image, the average similarity refers to the average similarity value obtained by one-to-one matching analysis of all identification keywords recorded by the two target images; marking all target images corresponding to the average similarity difference in each first-category target image being less than or equal to the difference threshold as key target images; Traverse all the first-category target images until the division is completed to generate various types of key target images.

3. The image recognition management method based on data analysis according to claim 1, characterized in that: The step of storing the associated images in a database corresponding to the identified keywords based on the annotation information includes the following specific steps: Extracting identification keywords from the annotation information of each type of key target image record, and combining the identification keywords in any number to generate keyword pairs under all conditions; the keyword pairs contain at least two identification keywords; The key target image is then stored in the database where the keyword pair under each condition is located, so that when the user searches with the keyword pair, the stored key target image is extracted based on the marked keyword pair in the database.

4. The image recognition management method based on data analysis according to claim 1, characterized in that: The step S3 comprises the following specific steps: Step S31: The image retrieval event refers to an event in which a user inputs keyword content related to an image based on a database and selects a target image; the retrieval factor refers to the keyword content recorded in the search box, and the retrieval result refers to the user retrieving the required target image based on the database and recording it as a successful retrieval; when the user processes the target image and the keyword content in the search box is unidirectional, the corresponding target image is output as the required target image; The unidirectionality means that the changes in the keyword content all satisfy the unique corresponding keyword pair in the annotation information; Step S32: extract the retrieval factors that record all key target images in the image retrieval event, match the retrieval factors to the annotation algorithm corresponding to the annotation information, obtain the number of image retrieval events U1 that record successful retrieval under each annotation algorithm and the total number of image retrieval events U2 that apply the annotation algorithm to execute; define the time period when all types of key target images are recorded in the image retrieval event as the effective period; use the formula: Z=k1×(U1 / U2)+k2×(R2 / R1); Calculate the effective feedback index Z of each type of annotation algorithm for each type of key target image; where R1 represents the number of keywords contained in the keyword pair with the highest success rate under the same algorithm, and R2 represents the maximum number of keywords contained in the keyword pair generated under the same algorithm. The success rate refers to the ratio of the number of successful retrieval events to the total number of image retrieval events for the same keyword pair; Where k1 and k2 represent the corresponding reference coefficients.

5. The image recognition management method based on data analysis according to claim 4 is characterized in that: The step S4 comprises the following: Sort the various annotation algorithms according to the effective feedback index Z value from large to small, and select the annotation algorithm ranked first as the best annotation algorithm for the target image of the corresponding category; The best image categories for real-time target image analysis include the following: Determine effective annotation information for the real-time target image; extract the number of identification keywords recorded in the effective annotation information, and match the target image with the same number of real-time identification keywords to form a first image set; Based on the first image set, the keyword similarity between each target image in the first image set and the real-time target image is calculated, and the target image corresponding to the maximum keyword similarity is selected as the best image category.

6. An image recognition management system based on data analysis, such as using an image recognition management method based on data analysis according to any one of claims 1 to 5, characterized in that: The management system includes an initial classification module, a labeling information analysis and storage module, an effective feedback index analysis module, an optimal labeling algorithm determination module and a real-time target image storage reminder module; The initial classification module is used to perform initial classification of target images on any image data set consisting of a plurality of images; The annotation information analysis and storage module is used to store the associated image in a database corresponding to the identification keyword based on the annotation information; The effective feedback index analysis module is used to analyze the effective feedback index of each category of target images corresponding to each type of annotation algorithm; The optimal labeling algorithm determination module is used to determine the optimal labeling algorithm for each category of target images. The real-time target image storage reminder module is used to analyze the best image category of the real-time target image and perform storage reminder of the annotation algorithm using the best annotation algorithm corresponding to the best image category.

7. The image recognition management system based on data analysis according to claim 6, characterized in that: The initial classification module includes a labeling algorithm implementation unit, a labeling keyword generation unit, a valid labeling information analysis unit, a first-category target image marking unit, and a key target image division unit; The annotation algorithm implementation unit is used to implement an annotation algorithm for the target image that can automatically generate annotation information based on the image content. The identification keyword generating unit is used to obtain identification keywords output by the annotation algorithm regarding the target image; The effective annotation information analysis unit is used to form annotation information corresponding to each target image by using the identification keywords in all the annotation algorithms; and select the annotation information containing the most identification keywords as the effective annotation information corresponding to the target image; The first-category target image marking unit is used to preliminarily classify target images containing the same total number of identification keywords into the same category of target images and mark them as first-category target images; The key target image division unit is used to mark all target images corresponding to the average similarity difference value less than or equal to the difference threshold value in each first-category target image as key target images.

8. The image recognition management system based on data analysis according to claim 6, characterized in that: The effective feedback index analysis module includes a retrieval success event analysis unit and an effective feedback index calculation unit; The retrieval success event analysis unit is used to extract the event that the user retrieves the required target image based on the database and records it as a retrieval success; The effective feedback index calculation unit is used to calculate the effective feedback index of each type of key target image corresponding to each type of labeling algorithm.

9. The image recognition management system based on data analysis according to claim 6, characterized in that: The real-time target image storage reminder module includes a keyword number matching unit, a similarity analysis unit and an optimal image category output unit; The keyword number matching unit is used to determine effective annotation information for the real-time target image; extract the number of identification keywords recorded in the effective annotation information, and match the target image with the same number of real-time identification keywords to form a first image set; The similarity analysis unit is used to calculate the keyword similarity between each target image in the first image set and the real-time target image; The optimal image category output unit is used to select the target image corresponding to the maximum keyword similarity value as the optimal image category.

Images