Mean shift, like other gradient ascent optimization methods, is susceptible to local maximum/minimum, and hence often fails to find the desired global maximum/minimum. For this reason, mean shift segmentation 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 the paper in order to improve the efficiency of the new algorithm. After mean shift vector is optimized using HBC algorithm, the optimal mean shift vector is updated using mean shift procedure. Experimental results show that new algorithm not only has higher convergence speed, but also can achieve more robust segmentation results.
Multi-Channel Sparsity Histogram based Particle Filter for Hand Tracking