2D Image segmentation by Hybridization of PSO and BBO

Image segmentation is an important research issue in image processing. In this paper, hybridizing of the PSO and BBO algorithm for 2-D image segmentation is implemented. The common features from PSO and BBO algorithm are used and then hybridized for the segmentation. The results are evaluated on the basis of parameters; PSNR and SSIM. The results depicts that the proposed hybrid algorithm performed well and produce better segmented 2D images . Keywords— Segmentation, PSO, BBO, 2D, Hybrid, PSO-BBO, Fitness function, habitat, crossover. INTRODUCTION It is widely used in analyzing the exactness and dimensions of an image. The slices of 2D images have like shapes which gives clue for segmentation of 2D image [1]. Image segmentation is used to recognize the each segment of the image more clearly. In this paper we are representing the 2D image segmentation by combined approach of PSO and BBO [2].Also comparing the results of 2D segmentation from PSO, BBO and Hybrid algorithm. PSO and BBO algorithms come under the category of swarm optimization. The concept of swarm optimization has arrived from the activities of social insects and birds. Social birds are characterized from their self organizing behavior and by finding the optimum paths through minimum communication. They can get information about surroundings and can interact with other birds indirectly through stigmergy. These all features characterise swarm intelligence. The two widely used swarm intelligence techniques are Particle Swarm Optimization and Biogeography Based Optimization.