Image segmentation method based on weight variation expectation maximization criterion

Abstract

The invention discloses an image segmentation method based on a weight variation expectation maximization criterion. The method comprises: first of all, extracting characteristic information of an image to be segmented, then describing the distribution of the image characteristic information by use of an expansion Gauss mixture model, based on the weight variation expectation maximization criterion, estimating the variation distribution of parameters of the expansion Gauss mixture model, after estimation is finished, obtaining a probability generated by each class to be divided from each pixel point, finally carrying out determination, and taking a sequence number corresponding to the maximum value in the probability values about each class of each pixel point as a class finally distributed to the pixel point so as to finish an image segmentation process. According to the invention, the segmentation quality and effect of a color image can be effectively improved, and the segmented image has quite good smoothness. By using the method, the over-fitting and under-fitting problems easily occurring in a conventional segmentation method based on a maximum likelihood criterion can be avoided.

Description

technical field [0001] The invention relates to an image segmentation method based on weighted variation expectation maximization criterion, which belongs to the technical field of image processing. Background technique [0002] Image segmentation is one of the key technologies in digital image processing. The task of image segmentation is to divide the input image into some independent regions, so that the same region has the same attributes, and different regions have different attributes. Image segmentation is the basis of further image recognition, analysis and understanding, and has been widely valued in both theoretical research and practical application. For the image segmentation problem, many methods have been proposed at present, but in view of the characteristics of images, especially RGB color images, which have many types, large amounts of data, and many changes, so far no segmentation method is suitable for all situations. The quality of the results also need...

AI Technical Summary

Problems solved by technology

But there are some problems: first, based on the maximum likelihood criterion, it is easy to cause over-fitting and under-fitting problems; second, because the mixture component score of GMM is the same as the number of regions to be segmented, and the latter is difficult to accurately determine before segmentation; On the other hand, the Gaussian mixture model needs to specify the number accurately, so once the mixture component is set inaccurately, it is easy to affect the segmentation quality

Finally, the segmentation method based on the maximum likelihood criterion only performs segmentation based on image pixel information without incorporating prior information. Therefore, it is necessary to improve the existing methods to further improve the effect and performance of the image segmentation system

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