Improved clustering criterion for image clustering with artificial bee colony algorithm

In this paper, a new objective function is proposed for image clustering and is applied with the artificial bee colony (ABC) algorithm, the particle swarm optimization algorithm and the genetic algorithm. The performance of the proposed objective function is tested on seven benchmark images by comparing it with the three well-known objective functions in the literature and the K-means algorithm in terms of separateness and compactness which are the main criterions of the clustering problem. Moreover, the Davies-Bouldin Index and the XB Index are also employed to compare the quality of the proposed objective function with the other objective functions. The simulated results show that the ABC-based image clustering method with the improved objective function obtains well-distinguished clusters

A survey on the applications of artificial bee colony in signal, image, and video processing

Artificial bee colony (ABC) algorithm is a swarm intelligence algorithm, which simulates the foraging behavior of honeybees. It has been successfully applied to many optimization problems in different areas. Since 2009, ABC algorithm has been employed for various problems in signal, image, and video processing fields. This paper presents the problems ABC algorithm has been applied in these fields and describes how ABC algorithm was used in the approaches for solving these kinds of problems