[1]WU Yi-quan,JI Shou-xin.Multithreshold selection for an image based on gray entropy and chaotic particle swarm optimization[J].CAAI Transactions on Intelligent Systems,2010,5(6):522-529.
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Multithreshold selection for an image based on gray entropy and chaotic particle swarm optimization

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 5 Number of periods: 2010 6 Page number: 522-529 Column: 学术论文—人工智能基础 Public date: 2010-12-25
Title:
Multithreshold selection for an image based on gray entropy and chaotic particle swarm optimization
Author(s):
WU Yi-quan1; 2;  JI Shou-xin1
1.School of Information Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China; 
2.State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, China
Keywords:
image segmentation;  threshold selection;  gray entropy;  quantified image histogram;  multithreshold;  particle swarm optimization of chaotic niche
CLC:
TP391.41； TN911.73
DOI:
-
Abstract:
The method of threshold selection based on maximal Shannon entropy depends only on the probability information from a gray image histogram and does not immediately consider the uniformity of the gray scale within the cluster. Considering these facts, a method of threshold selection based on maximal gray entropy was proposed. First, gray entropy was defined and the method of single threshold selection was given. Being different from maximal Shannon entropy based only on histogram distribution, the gray entropy reflects the uniformity of the gray scale immediately within the cluster. Then, the formulae of gray entropy based single threshold selection of a quantized image histogram were derived. Finally, the method of single threshold selection based on gray entropy was extended to multithreshold selection. A corresponding fast recurring algorithm was proposed. Furthermore, a particle swarm optimization algorithm with a chaotic niche was adopted to find the best multithreshold. Many experimental results show that, compared with the methods of single threshold selection based on maximal Shannon entropy and multithreshold selection based on maximal Shannon entropy with particle swarm optimization, segmented images of the suggested method are more accurate in edge and texture, and their visual effect is improved significantly.
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