Multi-feature fusion based color image retrieval method for HSV space image retrieval

Abstract

The invention discloses a multi-feature fusion based color image retrieval method for HSV space image retrieval. According to the method, a color image is transferred from an RGB space to an HSV space in the color image retrieval process, a gray-scale image is decomposed by utilizing non-subsample Shearlet shear waves, color histogram feature extraction and texture index spacing amplitude feature extraction are respectively performed by utilizing different scales of sub-bands obtained through decomposition, finally the similarity of different images is calculated by utilizing an Euclidean distance as an image similarity calculating method, and results are sorted and output according to the similarity from big to small. Compared with single color or texture retrieval, the retrieval accuracy is improved to the greatest degree, and accordingly the problem that the ideal image retrieval efficiency cannot be achieved according to individual color features or individual texture features is solved.

Description

technical field [0001] The invention belongs to the field of image processing, in particular to a color image retrieval method based on HSV space image retrieval based on multi-feature fusion, which can reduce computational complexity and improve retrieval efficiency. Background technique [0002] The previous image retrieval technology (TBIR) follows the traditional text retrieval technology, which does not consider the inherent color, texture, shape and other content characteristics of the image itself, but uses keywords to describe and retrieve images. There are the following defects: it takes a lot of time and manpower to manually label each image in the database; it is difficult to accurately describe the connotation of images with different contents using keywords; manual selection of keywords will contain strong subjectivity, which may cause problems in image understanding. The deviation directly affects the image retrieval effect. In order to overcome the above defe...

AI Technical Summary

Problems solved by technology

There are the following defects: it takes a lot of time and manpower to manually label each image in the database; it is difficult to accurately describe the connotation of images with different contents using keywords; manual selection of keywords will contain strong subjectivity, which may cause problems in image understanding. The deviation directly affects the retrieval effect of the image

There are problems of high computational complexity and low retrieval efficiency

Images