Adaptive mean shift algorithm based on hybridized bacterial chemotaxis

Mean shift is an effective statistical iterative algorithm. But size of bandwidth has great impact on the accuracy and efficiency of the algorithm. It not only decides the number of sampling points in the iteration, but also affects the convergence speed and accuracy of the algorithm. For this reason, adaptive mean shift algorithm based on hybridized bacterial chemotaxis(HBC) is proposed in this paper. In HBC, particle swarm operation algorithm (PSO) is introduced before bacterial chemotaxis(BC) works. And PSO is firstly introduced to execute the global search, and then stochastic local search works by BC. Meanwhile, elitism preservation is used in this paper in order to improve the efficiency of the new algorithm. In the proposed new algorithm, bandwidth is calculated by HBC algorithm in which bandwidth is calculated adaptively according to the local structure of sampling points. Experimental results show that the proposed algorithm can achieve more robust segmentation results.