Color image searching method based on quaternion exponential moment

Abstract

The invention discloses a color image searching method based on a quaternion exponential moment. The method comprises the following steps: using the quaternion exponential moment for decomposing a color image during a color image searching process; taking a moment value acquired by utilizing the quaternion exponential moment to decompose the color image as an image feature; utilizing a calculation method taking Euclidean distance as the similarity between images to calculate the similarity between different images. The quaternion exponential moment is simultaneously combined with a color feature and a texture feature, so that the color and shape features of the image can be effectively represented. The method has the characteristics of simple design, easiness in implementation and no need of manual marking during an image feature extracting process, so that the complex and easily mistaken manual marking process is avoided and the searching precision and efficiency are greatly increased.

Description

technical field [0001] The invention belongs to the field of image processing, in particular to a color image retrieval method based on quaternion exponent moments 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 b...

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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