Two-Dimension Maximum Entropy Image Segmentation Approach Based on Chaotic Optimization

Abstract

Chaotic optimization is a new optimization technique. Conventional two-dimension chaotic sequence is not a good way to two-dimension gray histogram image segmentation because it is proportional distributing in [0,1] times [0,1]. In order to generate a better chaotic sequence that is fit to two- dimension gray histogram. A chaotic sequence generating method is proposed based on Arnold chaotic system and Bezier curve generating algorithm. The main feature of the new chaotic sequence is that its distribution is approximately inside a disc whose center is (0.5,0.5), this means that the sequence is superior to Arnold chaotic sequences in image segmenting. As application, a two-dimension maximum entropy image segmentation method is presented based on chaotic optimization. Simulation results show that our method has better segmentation effect and lower computation time than the original two-dimension maximum entropy method.