Mean shift and neighborhood information based fuzzy C-mean image segmentation method

Abstract

The invention discloses a mean shift and neighborhood information based fuzzy C-mean image segmentation method, and mainly solves the problems of low segmentation accuracy and poor robustness of an existing image segmentation method. The method comprises the steps of (1) inputting a to-be-segmented image; (2) calculating a cluster number and an initial cluster center by adopting a mean shift algorithm; (3) performing initialization; (4) calculating a weight of an neighborhood image block in the to-be-segmented image; (5) calculating a weighted fuzzy factor weight of each pixel point in the to-be-segmented image; (6) performing cluster iteration; (7) judging whether an iterative stop condition is met or not; and (8) generating a segmented image. According to the method, the neighborhood information of the image is fully utilized, so that the method is high in noise robustness and the accuracy of image segmentation is greatly improved.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a fuzzy C-means image segmentation method based on mean shift and neighborhood information in the technical field of image segmentation. The present invention obtains the initial number of clusters and cluster centers through the mean shift method, and uses the neighborhood information to smooth the membership degree of the pixel points in the clustering iteration, realizes the segmentation of the image, and can be used for the extraction of image feature targets . Background technique [0002] Fuzzy clustering analysis is one of the main techniques of data mining, among which fuzzy C-means clustering method is the most widely used fuzzy clustering method. Applying fuzzy clustering to image segmentation is a hot research direction in the field of image segmentation in recent years. The process of image segmentation is to treat each pixel as a data point, and the ...

AI Technical Summary

Problems solved by technology

However, this method still has shortcomings. Because this method uses a non-robust Euclidean distance, the segmentation process is more sensitive to noise points and less robust to noise.

However, the disadvantage of this method is that due to the use of the traditional fuzzy C-means clustering method, the influence of the neighborhood information of pixels on the clustering process is not considered, resulting in the method not being able to correctly segment the density distribution. balanced data set

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