Color image clustering segmentation method based on multi-scale perception characteristic of human vision

Abstract

Provided is a color image clustering segmentation method based on the multi-scale perception characteristic of human vision. The method is characterized by comprising the following steps: firstly, segmenting a CIELAB color space into two parts through a cylinder with (a, b) as the circle center and Rn as the radius, wherein a=0 and b=0; secondly, segmenting an image into segments with a certain density and a certain size according to the traditional image segmentation clustering algorithm; thirdly, calculating the average color vector value of each clustering segment and projecting each vector onto the ab plane; fourthly, calculating the length of the vector, projected onto the ab plane, of the average color vector value of each clustering segment; fifthly, classifying the clustering segments into different measure spaces according to the lengths of the vectors; sixthly, calculating the included angle between the vectors of every two adjacent segment classes according to the formula shown in the specification; seventhly, clustering the segments meeting conditions with the formula as the criterion; eighthly, repeating the third step to the six step until convergence. By means of the method, the clustering effect and the anti-jamming capability of the image are improved.

Description

technical field [0001] The present invention relates to an image clustering processing method, especially an image clustering and segmentation method that can be widely used in the fields of robot vision, such as space recognition of outdoor and indoor scene images, target measurement in large spaces, target tracking and positioning, etc., specifically It is a color image clustering and segmentation method based on the multi-scale perception characteristics of human vision. Background technique [0002] The spatial recognition of outdoor and indoor scene images depends on the cognition and recognition of scene image objects. Therefore, how to effectively cluster the ground, walls, ceilings, sky, buildings, trees and other objects in the scene will It is the key to the successful realization of image application fields such as robot vision for space recognition of outdoor and indoor scene images, large space target recognition, search, measurement, tracking and positioning. ...

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Problems solved by technology

[0003] The purpose of the present invention is to aim at the existing image clustering method when the light in the scene image is more complicated, the clustering effect of methods such as the Euclidean distance of the traditional color space, Bhattacharyya distance and the clustering effect of human vision to the scene image are comparable There is a big difference in ratio

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