Clustered image splitting method based on particle swarm optimization and spatial distance measurement

Abstract

The invention discloses a clustered image splitting method based on particle swarm optimization and spatial distance measurement. The clustered image splitting method mainly solves the problem that the existing clustered image splitting technology has local misdivision phenomena and multiple area miscellaneous points. The clustered image splitting method includes the steps: (1) inputting an original image, extracting pixel characteristics and conducting watershed splitting, (2) calculating an adjacent matrix according to a split area and generating clustered data, (3) using the clustered data to initialize a cluster at random, (4) calculating membership matrixes and adaptability values of the cluster, upgrading individuals and overall to be optimum, and evolving the cluster, (5) upgrading iterations, outputting the best membership matrix if the preset maximum iterations are achieved, and continuously executing the step (4) if the preset iterations are not achieved, and (6) according to the best membership matrix, marking according to the maximum probability principle to obtain the splitting result. Compared with the prior art, the clustered image splitting method has the advantages of being good in area coincidence, high in splitting accuracy and capable of being used for object identification on an SAR image.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to a segmentation method involving texture images and SAR images, which can be applied to target recognition. Background technique [0002] Image segmentation is one of the key technologies of image processing. The result of image segmentation is to divide the image into several parts, each part represents a different feature in the image, and mark the same part of pixels as the same value. The existing image segmentation methods mainly include methods based on regions, methods based on edge detection, methods based on clustering and so on. At present, people mostly use methods based on cluster analysis for image segmentation. The method based on clustering analysis is to use the known training sample set to find the decision-making classification points, lines or surfaces in the feature space of the image, and then map them back to the original image space to realize the...

AI Technical Summary

Problems solved by technology

However, in the clustering process, because only the attributes of the region are considered, such as grayscale, texture, etc., the relationship between regions is ignored, and the integrity of spatial information is lacking, resulting in more noise points in the region and consistent regions. Poor performance and unsatisfactory segmentation

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