A fabric matching method and system based on multi-modal information matching

By using semantic structuring enhancement driven by a terminology vocabulary for the fabric industry and salient region segmentation, combined with multi-channel texture fidelity processing, the problem of inaccurate semantic matching between fabric images and text descriptions is solved, achieving high-precision fabric retrieval and matching, suitable for complex shooting environments and non-standard description scenarios.

CN120976585BActive Publication Date: 2026-06-19ZHIYI TECH
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Patent Information

Application Number
CN202511051916.4
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-07-29
Publication Date
2026-06-19
Estimated Expiration
2045-07-29

AI Technical Summary

Technical Problem

Existing technologies suffer from incomplete semantic coverage, inaccurate texture representation, and low matching accuracy in matching fabric images with text descriptions. In particular, it is difficult to achieve accurate fabric positioning and semantic consistency matching between images and text under complex shooting environments and non-standard descriptions.

Method used

A multimodal information matching method is adopted, which uses semantic structuring enhancement processing driven by a terminology vocabulary of the fabric industry and a saliency-guided cross-modal matching architecture, including semantic regularization recognition, multi-channel texture fidelity enhancement, saliency region segmentation and multi-factor ranking, to generate global semantic vectors of images and sub-semantic vectors of texture regions, thereby achieving high-precision alignment between fabric images and text descriptions.

Benefits of technology

It improves the accuracy and response speed of fabric retrieval, enhances the accuracy and robustness of fabric matching in open descriptive contexts, and significantly improves the professionalism and commercial applicability of matching results, especially under complex shooting conditions and non-standard descriptions.

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Abstract

This invention relates to the field of intelligent fabric matching and recognition technology, and discloses a fabric matching method and system based on multimodal information matching. The method includes: semantically structuring and enhancing user input text; acquiring the original fabric image and performing mask extraction, texture enhancement, and normalization processing; calculating the matching degree based on a cross-modal semantic alignment mechanism, and outputting fabric recommendation results by combining multi-factor joint ranking. Compared to existing technologies that use simple text retrieval or single-channel feature matching, especially when user descriptions are non-standard, fabric image textures are complex, or structures are repetitive, failing to achieve accurate fabric location and consistent semantic matching between text and images, this application improves fabric retrieval accuracy in open descriptive contexts by introducing an industry ontology-driven semantic enhancement mechanism and a saliency-guided cross-modal matching architecture, achieving multi-granular modeling and fine-grained semantic alignment of fabric semantic features.
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