%0 Journal Article
%T Two-dimensional Tsallis gray entropy image thresholding using chaotic particle swarm optimization or decomposition<br>利用混沌PSO或分解的2维Tsallis灰度熵阈值分割
%A Wu Yiquan
%A Wu Shihua
%A Zhang Xiaojie
%A <br>吴一全
%A 吴诗婳
%A 张晓杰
%J 中国图象图形学报
%D 2012
%I 
%X The method of threshold selection based on two-dimensional maximal Shannon or Tsallis entropy only depends on the probability information from gray histogram of an image, and does not immediately consider the uniformity of within-cluster gray scale. The segmentation effect of the Tsallis entropy method is superior to that of the Shannon entropy method. Thus, a two-dimensional Tsallis gray entropy thresholding method based on chaotic particle swarm optimization(PSO) or decomposition is proposed. First, a one-dimensional thresholding method based on Tsallis gray entropy is given and extended to the two-dimensional case. The corresponding formulae and its recursive algorithm for threshold selection based on the two-dimensional Tsallis gray entropy are derived. Then a chaotic particle swarm optimization algorithm is used to find the optimal threshold of the two-dimensional Tsallis gray entropy method. The recursive algorithm is adopted to avoid the repetitive computation of the fitness function in an iterative procedure. As a result, the computing speed is improved greatly. Finally, the computations of threshold selection method based on two-dimensional Tsallis gray entropy are converted into two one-dimensional spaces, which further reduces the computational complexity from O(L2) to O(L). The experimental results show that, compared with the two-dimensional maximal Shannon entropy method, the two-dimensional maximal Tsallis entropy method and the two-dimensional Tsallis cross entropy method, the two methods proposed in this paper can significantly improve image segmentation performance and algorithmic running speed.
%K image segmentation
%K threshold selection
%K two-dimensional Tsallis gray entropy
%K chaotic particle swarm optimization
%K decomposition
%K recursive algorithm<br>图像分割
%K 阈值选取
%K 2维Tsallis灰度熵
%K 混沌粒子群优化
%K 分解
%K 递推算法
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=D06194629680C940ACE75262F54B9D85&aid=0EB9A27D923E916637F0FF5135BE44DE&yid=99E9153A83D4CB11&vid=BCA2697F357F2001&iid=5D311CA918CA9A03&sid=F52FA2254444BE97&eid=A02B0E6E62BE4F0C&journal_id=1006-8961&journal_name=中国图象图形学报&referenced_num=0&reference_num=18