Image segmentation method based on overall manifold prototype clustering algorithm and watershed algorithm

Abstract

The invention discloses an SAR image segmentation method based on an overall manifold prototype clustering algorithm and a watershed algorithm, which mainly solves the problems of large calculated amount and unstable segmentation result of the traditional clustering segmentation method. The method comprises the following realizing steps of: (1) setting a termination condition for a running parameter; (2) inputting an image to be segmented and carrying out rough segmentation on the image to be segmented; (3) extracting the characteristics of an image block obtained after rough segmentation; (4) adopting the characteristics of the image block as data to be processed to select an initial clustering center; (5) calculating a manifold distance between any two data points to be processed; (6) adopting the manifold distance between the data to be processed as similarity measurement to carry out clustering on the data to be processed; (7) updating the clustering center; (8) judging whether the termination condition is reached or not, if not, turning to the step (6); otherwise, outputting a segmentation result. The invention has the advantages of short use time and accurate and stable segmentation result and can be used for the technical field of image reinforcement, mode recognition, target tracing, and the like.

Description

technical field [0001] The invention belongs to the technical field of image processing, relates to image segmentation, and can be used in technical fields such as image enhancement, pattern recognition, and target tracking. Background technique [0002] Image segmentation is an important step in 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. For the problem of image segmentation, researchers have proposed many methods, but in view of the characteristics of many types of images, large amount of data, and many changes, so far there is no image segmentation method suitable for all situations. As a means of image segmentation, data clustering has been widely used. [0003] As an important data analysis method, clustering has been widely concerned in many fields. It is a process of distinguishing and classifying data ac...

AI Technical Summary

Problems solved by technology

[0008] The technical solution of the present invention is to introduce the manifold distance into the clustering algorithm to achieve better clustering performance, and for large-scale problems such as image processing, a segmentation method of first rough segmentation and then subdivision is designed, and a prototype-based The clustering method solves the problem of high computational complexity of the manifold distance, and uses the discrete wavelet transform subband energy of the image as the clustering data to obtain a new image segmentation method

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