Network image retrieval method based on semantic analysis

Abstract

The invention relates to a network image retrieval method based on semantic analysis, which is used for extracting low-level features. Content-based image retrieval is performed on each type of feature to find out a visually-similar network image set. The related text information is used for semantic learning corresponding to each image in the network image set corresponding to each image in the network image set to obtain the semantic expression for the image query. The semantic consistency of the retrieval image set corresponding to various features on the text information is judged, the semantic consistency is used to measure the description capacities of various features, to endow the description capacities with different degree s of confidence. The semantics and semantic consistency of the image query are adopted to perform text-based image retrieval in the image base to obtain the semantic relevance of each image in the image base and the image query; the low-level features are adopted to perform content-based image retrieval on the image base to obtain the visual relevance of the each image in the image base and the image query; the semantics is fused with visual relevance through a linear function to ensure the image for the user to have both semantic and visual relevance.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to a network image retrieval method based on semantic analysis. Background technique [0002] With the rapid development of information technology, multimedia information has expanded rapidly. Image, as a kind of multimedia information with rich connotation and direct expression, has been paid attention to by people for a long time. However, due to the rapid increase of images on the Internet, taking the Google image search engine as an example, its retrievable images have exceeded 1 billion. How to find the image that best meets the user's needs through effective retrieval among the vast number of images has become an urgent problem to be solved. At present, there are two main techniques of image retrieval: text-based image retrieval and content-based image retrieval. [0003] The text-based image retrieval system indexes the text information around network images, such a...

AI Technical Summary

Problems solved by technology

However, due to the "semantic gap" between the underlying features of the image and the high-level semantics, visually similar images often have large differences at the semantic level, which violates the user's retrieval needs and greatly restricts content-based image retrieval. The development of image retrieval technology

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