Genetic cluster image cutting method based on multiple partial searches

Abstract

Provided is a genetic cluster method based on multiple partial searches. Evolution in the genetic clustering process is supported through the method with the three partial searches including cluster combining, cluster splitting and k average value operation, and the searches have different characteristics according to search tasks. The first two partial searches are used for partially increasing the number of clusters in a solution, and how the first two partial searches are used is decided in the clustering process in a self-adaptive mode. The third partial search is used for improving the clustering center of the solution. The three partial searches are used for mutually cooperating and competing from the aspect of complementation to partially improve the solution in genetic evolution so that the common optimization target can be completed. The decision space can be rapidly and effectively found, and therefore the proximity global optimal clustering result is given. The genetic cluster method based on the multiple partial searches is applied to image cutting, and the precise cutting effect is achieved.

Description

technical field [0001] The present invention relates to the field of image processing and application, intelligent method and data clustering, in particular to a genetic clustering image segmentation method based on multi-local search. The genetic clustering method based on multi-local search uses a genetic algorithm to The adaptive method combines three different local searches to quickly and efficiently search the decision space, thus giving clustering results close to the global optimum. Background technique [0002] Data clustering is one of the most challenging problems in machine learning. Its goal is to divide a dataset into multiple clusters such that objects within the same cluster are similar to each other and objects from different clusters are dissimilar to each other. Data clustering is a basic tool for unsupervised learning and has been widely used in many scientific and engineering fields, such as big data analysis and computer vision. In general, clustering...

AI Technical Summary

Problems solved by technology

These hybrid genetic clustering methods can improve operational efficiency, but these methods usually use a single local search, which greatly limits the room for efficiency improvement

In addition, for complex clustering problems, often involving a large number of local optimal solutions, traditional genetic clustering methods or hybrid genetic clustering methods are often unable to find the optimal clustering results

This is mainly due to their inability to maintain the diversity of solutions during the evolution process, which leads to premature convergence of the entire population to a local optimal solution

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