[1]WU Yiquan,WANG Kai,CAO Pengxiang.Two-dimensional asymmetric tsallis cross entropy image threshold selection using bee colony optimization[J].CAAI Transactions on Intelligent Systems,2015,10(1):103-112.[doi:10.3969/j.issn.1673-4785.201403040]
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Two-dimensional asymmetric tsallis cross entropy image threshold selection using bee colony optimization

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 10 Number of periods: 2015 1 Page number: 103-112 Column: 学术论文—人工智能基础 Public date: 2015-03-25
Title:
Two-dimensional asymmetric tsallis cross entropy image threshold selection using bee colony optimization
Author(s):
WU Yiquan1; 2; 3;  WANG Kai1;  CAO Pengxiang1
1. College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;
2. Jiangsu Key Laboratory of Quality Control and Further Processing of Cereals and Oils, Nanjing University of Finance Economics, Nanjing 210046, China;
3. Jiangsu Provincial Key Laboratory of Pulp and Paper Science and Technology, Nanjing Forestry University, Nanjing 210037, China
Keywords:
image segmentation; threshold selection; two-dimension; Tsallis cross entropy; recursive algorithms; bee colony optimization; inter-regional contrast
CLC:
TP391.4
DOI:
10.3969/j.issn.1673-4785.201403040
Abstract:
Cross entropy can measure the difference between the original image and its segmentation result. Compared with Shannon cross entropy, Tsallis cross entropy, in which a parameter q is introduced, provides flexibility and universality for the segmentation of image threshold. The asymmetric Tsallis cross entropy has more concise expression form. Therefore, a method of threshold selection is proposed based on the two-dimensional asymmetric Tsallis cross entropy using bee colony optimization. Firstly, the asymmetric Tsallis cross entropy is introduced and the threshold selection formulae based on the two-dimensional asymmetric Tsallis cross entropy are derived. Recursive algorithms are used to calculate the intermediate variables involved in criterion function for threshold selection and a lookup table is built to eliminate the redundant operations. The optimal two-dimensional threshold is searched by the bee colony algorithm. A large number of experiment results showed that the proposed method is greatly improved in terms of subjective visual effect and inter-regional contrast evaluation indicators compared to the relevant methods, such as the two-dimensional maximum Shannon entropy method, the two-dimensional Shannon cross entropy method, the two-dimensional Tsallis entropy method, and the two-dimensional symmetrical Tsallis cross entropy method. It can segment objects more accurately and has a faster running speed.
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