Synergy of adaptive bacterial foraging algorithm and Particle Swarm Optimization algorithm for image segmentation

Many practical applications such as medical image segmentation, object detection, recognition tasks and video surveillance have need for accurate image segmentation techniques. Hence image segmentation is an important technique for image processing which is regarded as first step for image analysis. In this paper an image segmentation technique based on Bacterial Foraging (BF) and Particle Swarm Optimization (PSO) algorithm is addressed. Initially adaptation is done on BF algorithm by computing the step length using the number of variables in the search space. Further, on exhaustive analysis of BF algorithm, it was revealed that the tumble behavior will lead to random delay in searching optimal solutions and premature convergence. This synergy algorithm makes use of PSO in providing social information and adaptive BF algorithm in finding new optimal threshold values using elimination and dispersal. The proposed method has been applied to few benchmark images with promising results.