FCM (Fuzzy Cognitive Map) texture image segmentation method based on spatial neighborhood information

Abstract

The invention discloses an FCM (Fuzzy Cognitive Map) texture image segmentation method based on spatial neighborhood information, which belongs to the technical field of image processing. The invention mainly solves the problems of poor consistency of segmentation areas and low segmentation accuracy in the traditional texture image segmentation method. The method comprises the steps of: (1) extracting the features of texture images to obtain a feature data set X of the images; (2) clustering the feature data set X; and (3) calculating the probability of a data point belonging to a certain class according to a membership matrix obtained by clustering output, and marking the class of each data point according to a maximum probability principle to complete segmentation. Compared with other classical segmentation methods, the segmentation method can maintain the area consistency of the texture images in a better way and improve the segmentation accuracy, and the segmentation result conforms to the vision of a person. The method can be used for texture image segmentation and computer object identification.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to a clustering and segmentation method, which can be used for the segmentation of texture images. Background technique [0002] Texture image segmentation is a classic research topic in pattern recognition and computer vision. So far, there is no general and effective image segmentation method that can meet different needs. Separate different regions with special meaning in the image, these regions are not intersected with each other, and each region satisfies specific characteristics. Segmentation can also be considered as a problem of clustering the pixels of the image. According to the grayscale or texture characteristics of the pixels, it can be judged which sub-category all pixels belong to. In this clustering process, the feature of each pixel corresponds to Cluster samples, and each image region corresponds to a cluster. Fuzzy segmentation algorithm has attracted ...

AI Technical Summary

Problems solved by technology

However, the traditional FCM algorithm does not consider the spatial neighborhood information of pixels and the relationship between adjacent pixels, lacking the integrity of spatial information, but only clusters all samples as scattered sample points, so that in fact Valuable pixel neighborhood information is ignored

Adjacent pixels have similar eigenvalues ​​and are more likely to belong to the same class. Therefore, the standard FCM algorithm is very sensitive to noise, resulting in segmentation results that cannot maintain good regional consistency, and there are many noise points in the region. Segmentation results are not ideal

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