Thresholding based on reciprocal entropy and chaotic particle swarm optimization

Method based on information entropy is a class of important threshold selection method, but the existing maximum entropy method has the problem of undefined value. Thus, the threshold selection method based on reciprocal entropy is proposed. Firstly, reciprocal entropy is defined and onedimensional threshold selection method is given. The algorithm formulae for reciprocal entropy threshold selection based on two-dimensional histogram vertical segmentation and oblique segmentation is derived. In view that the computational burden of two-dimensional reciprocal entropy segmentation is large, Niche chaotic mutation particle swarm optimization(NCPSO) is adopted to find the optimal threshold. It avoids algorithm premature and improves searching accuracy and efficiency. The experimental results show that oblique segmentation method of two-dimensional reciprocal entropy has advantages over vertical segmentation method both on antinoise and running time. Compared with two-dimensional maximum entropy method based on particle swarm optimization(PSO), two-dimensional reciprocal entropy oblique segmentation method based on NCPSO is reduced by 40% or so in processing time, and achieves superior segmentation quality.