Color image searching method based on multiple features

Abstract

The invention discloses a color image searching method based on multiple features. The method comprises the following steps: during a process of searching the color image, extracting R,G, B channels of a to-be-searched color image, respectively calculating an exponential moment value of each channel and taking the exponential moment value as a color feature; converting the color image from an RGB color space into an HIS color space, utilizing an I component to calculate a local corner phase feature and taking the local corner phase feature as a textural feature; comprehensively performing color image searching through the color feature and the textural feature. The color information and the textural information of the image are combined with each other, so that the method provided by the invention can be effectively applied to the color image searching, the precision of the searching result is ensured, the method has the characteristics of simple calculation, no need of manual treatment on to-be-searched image, strong universality, low coupling, and the like, and the practicability for digital image searching is enhanced.

Description

technical field [0001] The invention belongs to the field of image processing, in particular to a multi-feature-based color image retrieval method that can effectively improve retrieval accuracy and efficiency. Background technique [0002] Text-based image retrieval technology (TBIR) follows the traditional text retrieval technology. It does not consider the inherent color, texture, shape and other content characteristics of the image itself, but uses keywords to describe the image. Retrieve related images in the form of . This technology has the following two defects: firstly, due to the continuous expansion of the current image database, it takes a lot of time and manpower to manually label each image in the database; In addition, the process of manually selecting keywords will contain strong subjectivity, which may cause deviations in image understanding and directly affect the retrieval effect of images. In order to overcome the difficulties brought by text-based imag...

AI Technical Summary

Problems solved by technology

This technology has the following two defects: firstly, due to the continuous expansion of the image database, it takes a lot of time and manpower to manually label each image in the database; Connotation, and the process of manually selecting keywords will contain strong subjectivity, which may cause deviations in image understanding and directly affect the retrieval effect of images

[0003] In image visual features, color and shape are the two most important features. However, the existing image feature extraction methods often deal with the two separately. There are many retrieval methods for color features, such as based on traditional histograms, color Correlogram, color matrix, spatial distribution method based on main color, etc.; contour-based and region-based retrieval methods for shape features, such as wavelet transform Gaussian distribution method, feature moment (Zernike moment, Legendre moment, etc.) method, etc., However, the retrieval efficiency of individual color features or trait features is low, which is far from meeting the requirements of image retrieval efficiency.

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