Multi-threshold Selection Using Maximum Reciprocal Entropy / Reciprocal Gray Entropy

The existing threshold selection methods based on Shannon entropy have the defects of undefined value and zero value, and they do not consider the uniformity of the gray scale within the object cluster and background cluster. In view of the above problems, the methods of multi-threshold selection based on maximum reciprocal entropy / reciprocal gray entropy and adaptive chaotic variation particle swarm optimization (ACPSO) are proposed for images including multiple objects or backgrounds in this paper. Firstly, the method of single threshold selection based on maximum reciprocal entropy is extended to multi-threshold selection. Then, reciprocal gray entropy is defined. The formulae of single threshold selection and multi-threshold selection based on maximum reciprocal gray entropy are derived. Finally, to find the optimal multiple thresholds quickly and accurately, the algorithm steps of multi-threshold selection based on reciprocal entropy / reciprocal gray entropy and ACPSO are given. The experimental results show that, compared with the existing related method, which is the method of multi-threshold selection based on maximum Shannon entropy and particle swarms optimization (PSO), the methods proposed in this paper have obvious advantages. Moreover, the methods have been used for image segmentation in infrared small target detection and satellite cloud image recognition, and they have excellent segmentation effect.