Fuzzy correlation synchronized image retrieval method based on color histogram and non-subsampled contourlet transform (NSCT)

Abstract

The invention relates to an image retrieval method. The method comprises the following steps of extracting color characteristics of an image by using a color histogram, and taking two characteristics such as a color vector of the color histogram and the height of a color column as the retrieval basis; calculating similarity by using the fuzzy membership function in a fuzzy set theory, judging the similarity through alpha fuzzy relation; introducing non-subsampled contourlet transform (NSCT) to extract the texture characteristic of the image at the same time; resolving the image by NSCT; extracting the mean value and standard deviation of sub-band coefficient in multiple directions of different layers and taking the mean value and standard deviation as feature vectors and index of the image in an image library, calculating similarity of the images by using the fuzzy membership function in a fuzzy set theory, wherein because the multiscale, multidirectionality and translation invariance property, great direction information can be kept after resolving, the method can completely describe the textural features of the image; finally, the two algorithms are combined, retrieving the image by using 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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