i am student of electronics engineering who is working in image processing field i had wrote a matlab code for image segmentation now i want to apply pso to optimize it, how can i do that? can you help me?
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Abstract
Particle swarm optimization is a stochastic global optimization algorithm that is based on swarm intelligence. Because of its excellent performance, particle swarm optimization is introduced into fuzzy entropy image segmentation to select the optimal fuzzy parameter combination and fuzzy threshold adaptively. In this study, the particles in the swarm are constructed and the swarm search strategy is proposed to meet the needs of the segmentation application. Then fuzzy entropy image segmentation based on particle swarm optimization is implemented and the proposed method obtains satisfactory results in the segmentation experiments. Compared with the exhaustive search method, particle swarm optimization can give the same optimal fuzzy parameter combination and fuzzy threshold while needing less search time in the segmentation experiments and also has good search stability in the repeated experiments. Therefore, fuzzy entropy image segmentation based on particle swarm optimization is an efficient and promising segmentation method.
Introduction
Particle swarm optimization (PSO) is a new evolutionary computing method that was developed by Kennedy and Eberhart in 1995 through the simulation of simplified social models of bird flocks . Due to its excellent performance, PSO has become one of the hotspots in evolutionary computing research and has been used in many applications such as function optimization, neural network training, and fuzzy control systems in recent years [2]. Image segmentation can be defined as the technique of dividing an image into disjoint homogeneous regions that usually contain similar objects of interest. And it is an important step in automatic image analysis and interpretation. However, due to the uncertainty and complexity of images encountered in actual applications, it is a very difficult task that affects directly the results of subsequent tasks such as feature extraction, object detection, and recognition. Because fuzzy set theory is an effective means of researching and processing fuzziness and uncertainty, fuzzy entropy has been used in image threshold segmentation [3] and [4]. Since the exhaustive search for all fuzzy parameter combinations is too costly, we introduced PSO into fuzzy entropy image segmentation to solve this optimal problem adaptively. This paper is organized as follows: in Section 2, the basic principle of PSO is described; in Section 3, the specific method of PSO applied in fuzzy entropy image segmentation is proposed; the experimental results are described and analyzed in Section 4 and conclusions are presented in Section 5.