Region based multiple features Integration and multiple-stage feedback latent semantic image retrieval method

Abstract

The invention discloses a latent semantic image retrieval method of region-oriented multi-feature integration and multi-level feedback. It uses result list returned by the initial keyword search, extracting a variety of region-oriented images characteristics, constructing attribute-image matrix, using latent semantic indexing algorithm to get the semantic space of image sets and semantic features of each image, and then using similar images by users feedback to construct or update image query vector, searching again the semantic space, calculating image semantics features and images inquiries vector similarity, getting outcome sets by descending order, and repeatable retrieval. The invention takes full advantage of image content information, making up for the deficiencies of the keyword search, and through the region-oriented multi-feature integration, enhances image content information from the bottom physical layer to the object layer, then further enhances to the semantic layer by HCI feedback, thereby reducing the gap between the image bottom features and high-level semantic, and allowing Web image retrieval to get higher retrieval accuracy.

Description

technical field [0001] The invention belongs to the field of multimedia information retrieval, and specifically relates to a latent semantic image retrieval method based on region-based multi-feature fusion and multi-level feedback. The method involves computer vision, matrix analysis, image retrieval and other fields, and can be directly used in the Web environment Image retrieval under integrated text and image content. Background technique [0002] The development of multimedia technology and network technology has led to the explosive growth of the number of images in the WWW. How to obtain the images that users need from the "rich" and "miscellaneous" Web image data makes it necessary to seek an accurate, comprehensive, concise, Flexible and intelligent image search technology has become an inevitable demand. The current image search engines mainly use text matching technology, which essentially transforms the image search problem into a traditional text retrieval prob...

AI Technical Summary

Problems solved by technology

At present, there are various methods and corresponding feedback strategies trying to achieve the goal of accurate search. However, the main idea is to gradually build a feature tree model from coarse to fine according to human understanding of images. The technology used tends to two directions: one is for In a specific field, the disadvantage is that the feature selection is single and the application is limited; one is that the processing range is too wide and the matching accuracy is too low

Correspondingly, most of its feedback models are still based on the underlying features, and the query requirements are updated by improving the query vector. However, the "semantic gap" is a fatal blow to this type of technology. Toml et al. in "A Picture is Worth a Thousand Keywords : Image-Based Object Search on aMobile Platform" mentioned in the article that image content is more efficient than text in image search, but it does not consider image multi-feature fusion retrieval

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