Hybridization of particle swarm optimization with the K-Means algorithm for image classification

The K-means algorithm is one of the widely used clustering algorithms in the image classification systems. However, the K-Means algorithm is easily trapped into the local optimal solutions. Several optimization techniques have been proposed to solve this problem such as genetic algorithms, simulated annealing and swarm intelligence. In this paper, we develop hybrid techniques using different particle swarm optimization (PSO) heuristics to optimize the K-Means algorithm and examine the reliability of parametric values for different variants of PSO and K-means algorithms. These PSO heuristics include linear inertia reduction, constriction factor, and dynamic inertia and maximum velocity reduction. The performance of these hybridization of PSO and the K-means algorithms was tested on the image segmentation. These PSO heuristics can make the K-means algorithm more stable for finding better solutions and less dependent on the initial cluster centers based on the preliminary experimental results.