Image retrieval method based on vocabulary tree level semantic model

Abstract

The invention discloses an image retrieval method, which is realized on the basis of a vocabulary tree level semantic model. Firstly, the characteristics of SIFT (scale-invariant feature transform) comprising color information of an image are extracted to construct the characteristic vocabulary tree of an image library, and a visual sense vocabulary describing image visual sense information is generated. Secondly, the Bayesian decision theory is utilized to realize the mapping of the visual sense vocabulary into semantic subject information on the basis of the generated visual sense vocabulary, a level semantic model is further constructed, and the semantic image retrieval algorithm based on content is completed on the basis of the model. Thirdly, according to relevant feedback of a user during a retrieval process, a positive image expandable image retrieval library can be added, and the high-level semantic mapping can be revised at the same time. Experimental results show that the retrieval method is stable in performance, and the retrieval effect is obviously promoted along with the increasing of feedback times.

Description

technical field [0001] The present invention relates to an image retrieval method, in particular to a content-based image retrieval method, especially a method that considers the high-level semantic information of the image contained in the image, and adds the user's understanding of the image content and the feedback of the retrieval result. Background technique [0002] With the rapid development of Internet and multimedia technology, content-based image retrieval (CBIR) has been extensively studied since the early 1990s. [0003] In the existing image retrieval technology, because it uses low-level features such as image texture, color, and shape as indexes to retrieve images, the information expressed by the low-level features of the image is inconsistent with the meaning of the user's understanding of these feature images, that is, semantic gap, so the retrieval effect often cannot meet the needs of users. [0004] If the high-level semantic information contained in th...

AI Technical Summary

Problems solved by technology

However, at present, the semantic mapping relationship in semantic retrieval is mostly established by manual annotation, and it is difficult to obtain a good mapping for the semantic information in the image to be retrieved, and the established mapping relationship cannot be automatically corrected with the feedback information retrieved by the user. Affect the improvement of retrieval effect

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