An improved particle swarm optimisation for video image segmentation (Q2221564)

Summary: Video image segmentation is the key step of objects classification and recognition. Threshold-based image segmentation algorithms, especially those based on the two-dimensional maximum entropy, have been studied by many scholars now. Although there are many algorithms that can get the entropy, they have their own weaknesses. For instances, the exhaustive search algorithm is time-consuming to implement the real-time image segmentation, the convergence rate of the traditional genetic algorithm is relatively slow, the standard particle swarm algorithm is prone to premature and difficult to get the optimal solution in the high-dimensional data space. In this paper, we proposed an improved particle swarm optimisation algorithm based on the law of universal gravitation to obtain the optimal entropy. Experimental results show that the improved algorithm is robust and obtain fast convergence rate.