Image-text retrieval method and system based on big data

By extracting and matching multi-dimensional feature of graphic information, the problem of being unable to dynamically adjust the filtering conditions and accurately match user needs in the existing technology is solved, and the high-precision graphic search effect is achieved.

CN119719393AActive Publication Date: 2025-03-28BEIJING AUGUST MELON TECHNOLOGY CO LTD
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Patent Information

Application Number
CN202510220800.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-02-27
Publication Date
2025-03-28
Estimated Expiration
2045-02-27

AI Technical Summary

Technical Problem

When facing massive graphic and text data, the existing technology lacks the ability to dynamically adjust the screening conditions according to the data characteristics and user needs, and cannot fully explore the internal connection between pictures and texts, and it is difficult to accurately match user needs.

Method used

By obtaining the graphic and text information to be retrieved, the image and text features are extracted, including color, texture, shape features and bag-of-word models, feature matching and correlation calculations are performed, multi-level filtering and matching are achieved, and the graphic and text search results are generated.

Benefits of technology

It realizes in-depth exploration of graphic and text information, improves the accuracy of search, and can accurately locate graphic and text that meet the requirements from massive data, significantly improving the quality of search results.

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Abstract

The invention discloses an image-text retrieval method and system based on big data, relates to the technical field of data processing, and solves the technical problems that the capability of dynamically adjusting screening conditions according to data characteristics and user requirements is lacked, the internal relation between images and texts cannot be fully mined, and the user requirements are difficult to accurately match. According to the method, multi-dimensional features such as colors, textures and shapes are extracted from image information, features are extracted from text information through a bag-of-word model, deep mining of image-text information is achieved, essential features of images and texts can be represented more comprehensively, the retrieval accuracy is improved, single matching, correlation calculation and screening and secondary matching are carried out, and the retrieval efficiency is improved. The text feature and image feature matching results are further combined and screened, finally, the combined retrieval results are matched and screened again, irrelevant information can be effectively eliminated through a multi-level screening mechanism, images and texts meeting requirements are accurately positioned from mass data, and the quality of the retrieval results is greatly improved.
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