Image segmentation method based on improved whale optimal fuzzy clustering

Abstract

The invention discloses an image segmentation method based on improved whale optimal fuzzy clustering, so as to mainly solve the problems of serious loss and long segmentation time after image information segmentation in the prior art. The method comprises the realization steps: 1, an image is inputted and the gray levels of all pixel points are acquired; 2, c clustering centers are selected to segment the image into c classes; 3, n whales are generated, wherein each whale has a c-dimensional vector, which represents a possible solution of a set of clustering centers; 4, with the reciprocal ofa fast fuzzy C-means clustering objective function as a fitness value, the optimal clustering center is searched; and 5, according to the searched set of clustering centers corresponding to the maximum fitness value, image segmentation is realized, pixel points with the gray levels in the same membership grade interval are classified as one class, and an image after segmentation is outputted. Through combining the optimization result and the fuzzy clustering image segmentation, the image segmentation effects are improved, and the method can be used for target detection, video monitoring and medical imaging.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to an image segmentation method for improving optimized fuzzy clustering of whales, which can be used for target detection, video monitoring and medical imaging. Background technique [0002] Image segmentation is one of the important technologies in image processing and computer vision. It divides an image into multiple non-overlapping regions according to characteristics such as gray level, shape and texture. The same region has similar characteristics, while different regions have different characteristics. have similar properties. Image segmentation is an important step from image processing to image analysis. Its purpose is to divide the image into several meaningful regions to facilitate subsequent processing, such as feature extraction and target recognition. There are many methods for image segmentation, such as segmentation methods based on cluster centers, ...

AI Technical Summary

Problems solved by technology

The artificial bee colony algorithm has strong exploration ability, but weak development ability, so the optimization efficiency in the iterative process is poor

According to the fuzzy set theory, the clustering image segmentation is realized. Compared with the traditional image segmentation method, the time required for the calculation process of the fuzzy clustering image segmentation algorithm based on the swarm intelligence algorithm is greatly shortened, but because the fuzzy clustering algorithm based on the swarm intelligence algorithm Image segmentation itself is difficult to balance the exploration ability and development ability in the optimization process, resulting in the problem that the best possible solution to the optimal clustering center cannot be found, resulting in serious loss of image information after clustering and segmentation. The effect is not good enough, further affecting the process of computer vision analysis

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