Fast image segmentation algorithm based on artificial bee colony optimization fuzzy clustering

Abstract

A fast image segmentation algorithm based on artificial bee colony optimization fuzzy clustering is proposed and optimizes the sensitivity of a traditional FCM algorithm to the initialization of clustering centers by using the intelligent behavior of bee colony in nature. The algorithm starts with bees looking for food sources. An improved fitness function value which is described in the description is used to express the nectar content of the food source, according to the greedy algorithm, the old and new food sources are selected. After the bee finishes searching, the information is transmitted to the follower bee. The bee chooses a food source according to the probability P related to the nectar amount, and at the same time, the bee searches the neighborhood near the food source. When the nectar amount is not improved after limited searches in the vicinity of a food source, the nectar source is abandoned, and the bees associated with the food source are replaced by scouting bees toindependently and randomly search for the nectar source, and the location of each food source represents a possible solution of the optimal clustering center of the image to be segmented.

Description

technical field [0001] The invention relates to a clustering algorithm, in particular to an image rapid segmentation algorithm for artificial bee colony optimization fuzzy clustering. Background technique [0002] In recent years, it is popular to solve image segmentation problems with clustering ideas. Among the clustering segmentation algorithms, the fuzzy C-means clustering algorithm (FCM) is widely used, but the initial value of the cluster center in the FCM clustering partition algorithm directly affects For the segmentation effect, if the cluster center is close to the final result, the number of iterations will be greatly reduced. Otherwise, the FCM algorithm will easily fall into a local minimum, making it difficult to segment the optimal image. [0003] The traditional fuzzy C-means clustering image segmentation algorithm is to iteratively optimize the objective function according to the weighted similarity measure of pixels and cluster centers to determine the best...

AI Technical Summary

Problems solved by technology

[0002] In recent years, it is popular to solve image segmentation problems with clustering ideas. Among the clustering segmentation algorithms, the fuzzy C-means clustering algorithm (FCM) is widely used, but the initial value of the cluster center in the FCM clustering partition algorithm directly affects Segmentation effect, if the cluster center is close to the final result, the number of iterations will be greatly reduced, otherwise, the FCM algorithm will easily fall into a local minimum, making it difficult to segment the optimal image

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