[1]MA Yinghui,WU Yiquan.Two-dimensional Renyi-gray-entropy image threshold selection based on chaotic cuckoo search optimization[J].CAAI Transactions on Intelligent Systems,2018,13(1):152-158.[doi:10.11992/tis.201607004]
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Two-dimensional Renyi-gray-entropy image threshold selection based on chaotic cuckoo search optimization

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 13 Number of periods: 2018 1 Page number: 152-158 Column: 学术论文—机器感知与模式识别 Public date: 2018-01-24
Title:
Two-dimensional Renyi-gray-entropy image threshold selection based on chaotic cuckoo search optimization
Author(s):
MA Yinghui1; 2;  WU Yiquan1; 3; 4; 5
1. College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China;
2. School of Information Engineering, Suqian College, Suqian 223800, China;
3. Key Laboratory of Manufacturing & Automat
Keywords:
image segmentation; threshold selection; cuckoo search algorithm; Renyi gray entropy; gray-gradient two-dimensional histogram; chaotic optimization; Arimoto entropy; Tsallis gray entropy
CLC:
TP391.41
DOI:
10.11992/tis.201607004
Abstract:
To further reduce the computational complexity of existing thresholding methods based on Renyi’s entropy, in this paper, we propose a method for threshold selection based on 2-D Renyi-gray-entropy image threshold selection and chaotic cuckoo search optimization. First, we derive the formula for a 1-D Renyi-gray-entropy threshold selection. Then, we build a 2-D histogram based on the grayscale and gray-gradient and derive a formula for 2-D Renyi-gray-entropy threshold selection based on this histogram. We use fast recursive algorithms to eliminate redundant computation in the threshold-selection criterion function. Finally, to achieve image segmentation, we search for the optimal threshold using the chaotic cuckoo search algorithm. The experimental results show that, compared with 2-D Arimoto-entropy thresholding method, the 2-D Renyi-entropy thresholding method based on particle swarm optimization, the 2-D Tsallis-gray-entropy thresholding method using chaotic particle swarm, and the 2-D Renyi-gray-entropy thresholding method based on the cuckoo search, our proposed method can segment objects more accurately and has a higher running speed.
References:
[1] MEHMET S, BULENT S. Survey over image thresholding techniques and quantitative performance evaluation[J]. Journal of electronic imaging, 2004, 13(1): 146-165.
[2] 吴一全, 孟天亮, 吴诗婳. 图像阈值分割方法研究进展20年(1994—2014)[J]. 数据采集与处理, 2015, 30(01): 1-23.
WU Yiquan, MENG Tianliang, WU Shihua. Research progress of image thresholding methods in recent 20 years (1994-2014)[J]. Journal of data acquisition and processing, 2015, 30(01): 1-23.
[3] 吴一全, 孟天亮, 王凯. 基于斜分倒数交叉熵和蜂群优化的火焰图像阈值选取[J]. 光学精密工程, 2014, 22(1): 235-243.
WU Yiquan, MENG Tianliang, WANG Kai. Threshold selection of flame image based on reciprocal cross entropy and bee colony optimization[J]. Optics and precision engineering, 2014, 22(1): 235-243.
[4] 吴一全, 张金矿. 二维直方图θ-划分最大平均离差阈值分割算法[J]. 自动化学报, 2010, 36(5): 634-643.
WU Yiquan, ZHANG Jinkuang. Image thresholding based on two-dimensional histogram θ-division and maximum between-cluster deviation criterion[J]. Acta automatica sinica, 2010, 36(5): 634-643.
[5] YIN Jun, WU Yiquan, ZHU Li. Multi-thresholding based on symmetric Tsallis-cross entropy and particle swarm optimization[J]. Advanced materials research, 2013, 760-762: 1457-1461.
[6] LIU Yaoyong, LI Shuguang. Two-dimensional arimoto entropy image thresholding based on ellipsoid region search strategy[C]//Proceedings of 2010 International Conference on Multimedia Technology. Ningbo, China, 2010: 1-4.
[7] MAHMOUDI L, ZAART A E. A survey of entropy image thresholding techniques[C]//Proceedings of the 2nd International Conference on Advances in Computational Tools for Engineering Applications. Beirut, Lebanon, 2012: 204-209.
[8] 朱磊. 自适应阈值分割技术及在工业视觉检测中的应用[D]. 无锡: 江南大学, 2014.
ZHU Lei. Adaptive threshold segmentation technology and its application in industrial visual inspection[D]. Wuxi: Jiangnan University, 2014.
[9] 曹建农. 图像分割的熵方法综述[J]. 模式识别与人工智能, 2012, 25(6): 958-971.
CAO Jiannong. Review on image segmentation based on entropy[J]. Pattern recognition and artificial intelligence, 2012, 25(6): 958-971.
[10] 张弘, 范九伦. 二维Arimoto熵直线型阈值分割法[J]. 光子学报, 2013, 42(2): 234-240.
ZHANG Hong, FAN Jiulun. Two-dimensional Arimoto entropy linear-type threshold segmentation method[J]. Acta photonica sinica, 2013, 42(2): 234-240.
[11] 吴一全, 王凯, 曹鹏祥. 蜂群优化的二维非对称Tsallis交叉熵图像阈值选取[J]. 智能系统学报, 2015, 10(1): 103-112.
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.
[12] 吴一全, 吴诗婳, 张晓杰. 利用混沌PSO或分解的2维Tsallis灰度熵阈值分割[J]. 中国图象图形学报, 2012, 17(8): 902-910.
WU Yiquan, WU Shihua, ZHANG Xiaojie. Two-dimensional Tsallis gray entropy image thresholding using chaotic particle swarm optimization or decomposition[J]. Journal of image and graphics, 2012, 17(8): 902-910.
[13] SARKAR S, DAS S. Multilevel image thresholding based on 2D histogram and maximum tsallis entropy—a differential evolution approach[J]. IEEE transactions on image processing, 2013, 22(12): 4788-4797.
[14] 潘喆, 吴一全. 二维RPAN Zhe, WU Yiquan. Fast recurring algorithms of image thresholding based on two-dimensional Renyi’s entropy[J]. Chinese journal of stereology and image analysis, 2007, 12(2): 93-97.yi熵图像阈值选取快速递推算法[J]. 中国体视学与图像分析, 2007, 12(2): 93-97.
PAN Zhe, WU Yiquan. Fast recurring algorithms of image thresholding based on two-dimensional Renyi’s entropy[J]. Chinese journal of stereology and image analysis, 2007, 12(2): 93-97.
[15] 顾晓清, 孙玉强, 侯振杰. 鲁棒的二维RGU Xiaoqing, SUN Yuqiang, HOU Zhenjie. Fast robust thresholding method based on two-dimensional Renyi’s entropy[J]. Computer science, 2012, 39(9): 284-288.yi熵图像阈值分割快速算法[J]. 计算机科学, 2012, 39(9): 284-288.
GU Xiaoqing, SUN Yuqiang, HOU Zhenjie. Fast robust thresholding method based on two-dimensional Renyi’s entropy[J]. Computer science, 2012, 39(9): 284-288.
[16] TEIXEIRA A, MATOS A, ANTUNES L. Conditional rényi entropies[J]. IEEE transactions on information theory, 2012, 58(7): 4273-4277.
[17] 雷博. 二维直线型RLEI Bo. Two-dimensional thresholding method based on linear-type Renyi entropy[J]. Journal of Xi’an university of posts and telecommunications, 2010, 15(3): 19-22, 33.yi熵阈值分割方法[J]. 西安邮电学院学报, 2010, 15(3): 19-22, 33.
LEI Bo. Two-dimensional thresholding method based on linear-type Renyi entropy[J]. Journal of Xi’an university of posts and telecommunications, 2010, 15(3): 19-22, 33.
[18] YANG X S, DEB S. Cuckoo search via Lévy flights[C]//Proceedings of 2009 World Congress on Nature and Biologically Inspired Computing. Coimbatore, India, 2009: 210-214.
[19] 周欢, 李煜. 具有动态惯性权重的布谷鸟搜索算法[J]. 智能系统学报, 2015, 10(4): 645-651.
ZHOU Huan, LI Yu. Cuckoo search algorithm with dynamic inertia weight[J]. CAAI transactions on intelligent systems, 2015, 10(4): 645-651.
[20] YANG X S, DEB S. Engineering optimisation by cuckoo search[J]. International journal of mathematical modelling and numerical optimisation, 2010, 1(4): 330-343.
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