Image segmentation method of semi-supervised kernel k-mean clustering based on constraint pairs

Abstract

The invention discloses an image segmentation method of semi-supervised kernel k-mean clustering based on constraint pairs. The image segmentation method comprises the implementation steps: (1) selecting an image; (2) extracting texture features of the image; (3) generating a clustering object data matrix; (4) segmenting the clustering object data matrix; (5) initializing a clustering center; (6) calculating a distance; (7) judging whether the distance meets a constraint condition or not, if the distance meets the constraint condition, executing the step (8), and if not, executing the step (5); (8) calculating a mean; (9) judging whether the mean meets a termination condition, if the mean meets the termination condition, executing the step (10), and if not, executing the step (6); (10) generating a segmented image. According to the image segmentation method of the semi-supervised kernel k-mean clustering based on the constraint pairs, the texture features of the image are extracted, the image segmentation method of the semi-supervised kernel k-mean clustering based on the constraint pairs is used for segmenting the texture features, the stability of image segmentation is improved, and the more accurate image segmentation result is obtained.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to an image segmentation method based on semi-supervised kernel K-means clustering of constraint pairs in the technical field of image segmentation. The invention can be used to segment texture images, natural images and SAR images to achieve the purpose of target recognition. Background technique [0002] In recent years, applying the idea of ​​semi-supervised clustering to image segmentation is a hot research direction in the field of image segmentation. Semi-supervised clustering mainly includes methods based on constraint pairs and methods based on seed sets. From the perspective of segmentation results, the process of image segmentation is to assign a label to each pixel, which reflects the category of the pixel in the segmentation result. As long as the labels of these features are found, the classification of pixels can be realized, and the result of image se...

AI Technical Summary

Problems solved by technology

This method can ensure the integrity of spatial information and reduce noise points, but the disadvantage of this method is that the segmentation result depends on the initialization of the cluster center, is sensitive to the selection of the initial cluster center, and is easy to fall into a local optimum, resulting in the segmentation The method is not robust, reducing the average accuracy over multiple split runs

Images