An image retrieval system and method based on multi-features and sparse representation

Abstract

The invention discloses an image retrieval system and an image retrieval method based on multi-feature and sparse representation. The system comprises a feature extraction module, a feature dictionary building module, a similarity measurement module, an information storage module and an inquiry interaction module, wherein the feature extraction module adopts shape and color combining image features, color enhanced Gaussian Laplace features (CLOG features) and SURF features; the feature dictionary building module compresses original features into overcomplete overcomplete features through an on-line dictionary learning algorithm, and overcomes the defect that the original features are too dense; the similarity measurement module introduces a sparse representation theory, compares the residual error size generated by original dictionary and relevant dictionary representation, and judges the similarity of the two images, and the problem of higher feature dependence of the traditional similarity measurement method is solved. The system and the method provided by the invention have the advantages that rotating, noise and illumination change images can be effectively retrieved, and the image retrieval robustness is obviously improved.

Description

technical field [0001] The invention relates to the technical field of content-based image retrieval, in particular to a robust image retrieval system and method based on multi-features and sparse representation. Background technique [0002] With the rapid development of computer, multimedia, network, and digital communication technologies, digital images, as one of the important carriers of various information, have penetrated into all aspects closely related to people's lives with their intuitive, vivid, easy-to-understand, and large-scale information. , has become an important way for people to obtain information. For digital images, how to effectively describe the content of the image, and then find the image that meets the user's needs from tens of thousands of image data is exactly what the image retrieval field needs to study. Due to the huge workload of manual labeling, keyword-based information retrieval technology is difficult to meet the requirements of users, w...

AI Technical Summary

Problems solved by technology

However, in real life, the images that need to be retrieved are often of low quality, noise pollution or compression deformation, and in special fields, such as the work of security and public security units, the ideal image acquisition conditions are difficult to meet, and the images to be retrieved There are often affine or lighting changes, and even occlusion and camouflage

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