General hybridized PSO with chaos for fast infrared image segmentation method

To detect infrared objects accurately, a fast infrared image segmentation method based on general hybridized PSO with chaos is proposed. The method of 2-D maximum fuzzy partition entropy can obtain better segmentation, because it takes advantage of gray and spatial neighboring information, and fuzziness of image also is taken into consideration. In essence, it is a typical nonlinear integer programming problem with huge searching space and many local optima. General hybridized PSO with chaos is based on general PSO, and it makes use of adaptive balance searching strategy. When the evolution stops, simulated annealing algorithm is introduced to select the current global optimum to be chaotic optimized for the sake of enhancing local searching ability and overcoming premature convergence. Experimental results show that the method can segment infrared image quickly and stably.