[1]YIN Yushan,WANG Lijin,YIN Yilong,et al.Backtracking search optimization algorithm assisted multilevel threshold for image segmentation[J].CAAI Transactions on Intelligent Systems,2015,10(1):68-74.[doi:10.3969/j.issn.1673-4785.201410008]
Copy

Backtracking search optimization algorithm assisted multilevel threshold for image segmentation

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 10 Number of periods: 2015 1 Page number: 68-74 Column: 学术论文—机器学习 Public date: 2015-03-25
Title:
Backtracking search optimization algorithm assisted multilevel threshold for image segmentation
Author(s):
YIN Yushan1;  WANG Lijin1; 2;  YIN Yilong1; 3;  WANG Binqing1;  ZHAO Wenting1;  XU Yunlong1
1. School of Computer Science and Technology, Shandong University, Jinan 250101, China;
2. College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou 350001, China;
3. School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan 250014, China
Keywords:
threshold method; backtracking search optimization algorithm; image segmentation; Otsu; Kapur; PSNR
CLC:
TP183
DOI:
10.3969/j.issn.1673-4785.201410008
Abstract:
The threshold method is a simple and effective image segmentation technique. However, the amount of calculation for solving threshold appears to be exponential amplification with the increase of threshold. This results in a huge challenge for multi-threshold image segmentation. This paper utilizes Otsu and Kapur methods as the target function in order to deal with image segmentation.In this paper, image segmentation is considered as an optimization problem whose objective function is formulated according to Otsu and Kapur methods, respectively. The backtracking search optimization algorithm is used to solve these two objective functions and to realize multi-threshold image segmentation. The proposed approach is applied to nature image segmentation and compared to other algorithms. The results showed that the multi-threshold image segmentation technique on the basis of backtracking search optimization algorithm is feasible and the segmentation effect is satisfactory.
References:
[1] 章毓晋. 图像工程 [M]. 北京:清华大学出版社, 2002:179-186.
[2] 刘国英, 马国锐, 王雷光, 等. 基于Markov随机场的小波域图像建模及分割—Matlab环境[M]. 北京:科学出版社, 2010: 6-15.
[3] OTSU N. A threshold selection method from gray-level histograms[J]. Automatica, 1975, 11: 23-27.
[4] PUN T. A new method for grey-level picture thresholding using the entropy of the histogram[J]. Signal Processing, 1980, 2(3): 223-237.
[5] KAPUR J N, SAHOO P K, WONG A K C. A new method for gray-level picture thresholding using the entropy of the histogram[J]. Computer Vision, Graphics and Image Processing, 1985, 29(3): 273-285.
[6] REDDI S S, RUDIN S F, KESHAVAN H R. An optimal multiple threshold scheme for image segmentation[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1984, 14(4): 661-665.
[7] KITTLER J, ILLINGWORTH J. Minimum error thresholding[J]. Pattern Recognition, 1986, 19(1): 41-47.
[8] CANNY J. A computational approach to edge detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986, PAMI-8(6): 679-698.
[9] ZIOU D, TABBONE S. Edge detection techniques: an overview[J]. International Journal of Pattern Recognition and Image Analysis, 1998, 8(4): 537-559.
[10] CHEN P C, PAVLIDIS T. Segmentation by texture using a co-occurrence matrix and a split-and-merge algorithm[J]. Computer Graphics Image Processing, 1979, 10(2): 172-182.
[11] CHEN S Y, LIN W C, CHEN C T. Split-and-merge image segmentation based on localized feature analysis and statistical tests[J]. CVGIP: Graphical Models and Image Processing, 1991, 53(5): 457-475.
[12] CHANG Y L, LI X. Adaptive image region-growing[J]. IEEE Transactions on Image Processing, 1994, 3(6): 868-872.
[13] BOYKOV Y, JOLLY MP. Interactive graph cuts for optimal boundary and region segmentation of objects in N-D images[C]//Proceedings of the Eighth International Conference on Computer Vision. Piscataway, NJ: IEEE, 2001:105-112.
[14] GRADY L. Random walks for image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(11): 1768-1783.
[15] KASS M, WITKIN A, TERZOPOULOS D. Snakes: active contour models[J]. International Journal of Computer Vision, 1988, 1(4): 321-331.
[16] OSHER S, SETHIAN J A. Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations[J]. Journal of Computational Physics, 1988, 79(1): 12-49.
[17] ROUT S, SRIVASTAVA M J. Multimodal image segmentation using a modified Hopfield neural network[J]. Pattern Recognition, 1998, 31(6): 743-750.
[18] 林开颜, 徐立鸿, 吴军辉. 快速模糊C均值聚类彩色图像分割方法[J]. 中国图像图像学报, 2004, 9(2): 159-163. LIN Kaiyan, XU Lihong, WU Junhui. A fast fuzzy C-M eans cluster ing for color image segmentation[J]. Journal of Image and Graphics, 2014, 9(2):159-163.
[19] CAO G B, WANG S L, WEI B Z, et al. A hybrid CNN-RF method for electron microscopy images segmentation[J]. Journal of Biomimetics Biomaterials and Tissue Engineering, 2013, 18(2): 1-6.
[20] WANG S L, CAO G B, WEI B Z, et al. Hierarchical level features based trainable segmentation for electron microscopy images[J]. Biomedical Engineering Online, 2013, 12(1): 59-72.
[21] TANG K Z, YUAN X J, SUN T K, et al. An improved scheme for minimum cross entropy threshold selection based on genetic algorithm[J]. Knowledge-Based Systems, 2011, 24(8): 1131-1138.
[22] YIN P Y. Multilevel minimum cross entropy threshold selection based on particle swarm optimization[J]. Applied Mathematics and Computation, 2007, 182(2): 503-513.
[23] ALI M, AHN C W, PANT M. Multi-level image thresholding by synergetic differential evolution[J]. Applied Soft Computing, 2014, 17: 1-11.
[24] AGRAWAL S, PANDA R, BHUYAN S, et al. Tsallis entropy based optimal multilevel thresholding using cuckoo search algorithm[J]. Swarm and Evolutionary Computation, 2013, 11: 16-30.
[25] SATHYA P D, KAYALVIZHI R. Optimal multilevel thresholding using bacterial foraging algorithm[J]. Expert System with Application, 2011, 38(12): 15549-15564.
[26] HORNG M H, LIOU R J. Multilevel minimum cross entropy threshold selection based on the firefly algorithm[J]. Expert System with Application, 2011,38(12): 14805-14811.
[27] MA M, LIANG J H, GUO M, et al. SAR image segmentation based on artificial bee colony algorithm[J]. Applied Soft Computing, 2011, 11(8): 5205-5214.
[28] CIVICIOGLU P. Backtracking search optimization algorithm for numerical optimization problems[J]. Applied Mathematics and Computation, 2013, 219: 8121-8144.
[29] MATHWORKS. Multilevel image thresholds using Otsu’s method[EB/OL]. [2014-08-22]. http://www.mathworks.cn/cn/help/images/ref/multithresh.html.
[30] SHI Y H, EBERHART R C. A modified particle swarm optimizer[C]//Proceedings of the 1998 IEEE World Congress on Computational Intelligence. Piscataway, NJ, 1998: 69-73.
Similar References:

Memo

-

Last Update: 2015-06-16

Copyright © CAAI Transactions on Intelligent Systems