Fuzzy-correlated asynchronous image retrieval method based on color histogram and NSCT (Non-Subsampled Contourlet Transform)

Abstract

The present invention relates to an image retrieval method. According to the method, color features of images are extracted by using a color histogram, two features, such as a color vector of the color histogram and the height of a color column, are used as retrieving bases, the degree of similarity is calculated by using a fuzzy membership function in a fuzzy set theory, the similarity is judged by using an alpha-level fuzzy relationship, meanwhile, texture features of the images are extracted by introducing non-subsampled contourlet transform (NSCT), the images are decomposed by using the NSCT, mean values and standard variances of subband coefficients in different levels and multiple directions are extracted as feature vectors which serve as indexes of images in an image library, the degree of similarity among the images is calculated by using the fuzzy membership function in the fuzzy set theory, powerful direction information is reserved after the images are decomposed due to the multi-scalability, multi-directionality and translation invariance of the images, thus, the texture features of the images can be described more comprehensively, and finally, the images are retrieved through combining two algorithms and applying comprehensive features.

Description

technical field [0001] The invention relates to an image retrieval method, in particular to an image retrieval method using color features extracted from color histograms and texture features extracted from non-subsampling contourlet transformation to perform integrated features. Background technique [0002] The content expressed by an image is very rich, it contains many aspects of features, and only using one feature cannot describe the entire content of the image. In addition, people's understanding of images is based on all the features that can be recognized by the human eye. The overall understanding of the image is not based on a certain feature. Therefore, it is often difficult to describe an image from only one aspect. Can not get a comprehensive description, and often can not achieve the ideal retrieval effect when the image changes greatly (enlargement, reduction, translation or rotation, etc.). [0003] At present, the single-feature image retrieval method can ...

AI Technical Summary

Problems solved by technology

In addition, people's understanding of images is based on all the features that can be recognized by the human eye. The overall understanding of the image is not based on a certain feature. Therefore, it is often difficult to describe an image from only one aspect. It cannot be fully described, and often cannot achieve ideal retrieval results when the image undergoes large changes (enlargement, reduction, translation or rotation, etc.)

[0003] At present, the single-feature image retrieval method can no longer meet the requirements of users, and comprehensive feature retrieval has been widely used.

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