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Active contour model via multi-swarm PSO with fuzzy rule-based adaptation of inertia factor

Active contour models, colloquially known as snakes, are quite popular for several applications such as object boundary detection, image segmentation, object tracking and classification. A snake is an energy minimizing curve that, starting from an initial contour, deforms iteratively thereby gradually moving towards the desired object boundary. Finally, it shrinks and wraps around the object. Therefore, the problem at hand is to find the contour that minimizes the snake energy. While energy minimization may be accomplished using traditional optimization methods, approaches based on nature-inspired evolutionary algorithms have been developed in recent years. One such evolutionary algorithm that has been used extensively in active contours is the particle swarm optimization (PSO). However, conventional PSO converges slowly and gets trapped in local minimum easily. The problem of local minimum results in inaccurate detection of concavities in the object boundary. This is taken care of by using multi-swarm PSO in which a swarm is set for every control point in the snake and then all the swarms search for their best points simultaneously and collaboratively through information sharing among them. The performance of the search process is further enhanced by using dynamic adaptation of the inertia factor. Inertia factor is used to balance the global and local search ability. In this paper, we propose to use a set of fuzzy rules to adjust the inertia weight on the basis of the current normalized snake energy and the current value of inertia. This improves the search for object concavities. Experimental results demonstrate the effectiveness of the proposed method compared to conventional approaches.