Color image segmentation using adaptive hierarchical-histogram thresholding


Autoři: Min Li aff001;  Lei Wang aff001;  Shaobo Deng aff001;  Chunhua Zhou aff003
Působiště autorů: Nanchang Institute of Technology, Nanchang, Jiangxi, PR China aff001;  Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang, Jiangxi, PR China aff002;  School of Life Sciences, Nanchang University, Nanchang, Jiangxi, PR China aff003
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article
prolekare.web.journal.doi_sk: 10.1371/journal.pone.0226345

Souhrn

Histogram-based thresholding is one of the widely applied techniques for conducting color image segmentation. The key to such techniques is the selection of a set of thresholds that can discriminate objects and background pixels. Many thresholding techniques have been proposed that use the shape information of histograms and identify the optimum thresholds at valleys. In this work, we introduce the novel concept of a hierarchical-histogram, which corresponds to a multigranularity abstraction of the color image. Based on this, we present a new histogram thresholding—Adaptive Hierarchical-Histogram Thresholding (AHHT) algorithm, which can adaptively identify the thresholds from valleys. The experimental results have demonstrated that the AHHT algorithm can obtain better segmentation results compared with the histon-based and the roughness-index-based techniques with drastically reduced time complexity.

Klíčová slova:

Algorithms – Birds – Clouds – Image analysis – Imaging techniques – Mathematical functions – Snakes – Valleys


Zdroje

1. Pare S, Bhandari AK., Kumar A, Singh GK, An optimal color image multilevel thresholding technique using grey-level co-occurrence matrix, Expert Systems With Applications. 2017;87:335–362. doi: 10.1016/j.eswa.2017.06.021

2. Yue XD., Miao DQ, Zhang N, Cao LB, Wu Q. Multiscale roughness measure for color image segmentation. Information Sciences. 2012; 216(24): 93–112. http://dx.doi.org/10.1016/j.ins.2012.05.025.

3. Aghbari ZA, Al-Haj R. Hill-manipulation: An effective algorithm for color image segmentation, Image & Vision Computing. 2006; 24(8): 894–903. doi: 10.1016/j.imavis.2006.02.013

4. Yen JC, Chang FJ, Chang S. A new criterion for automatic multilevel thresholding. IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society. 1995; 4: 370–378. doi: 10.1109/83.366472 18289986

5. Sahoo PK., Wilkins C, Yeager J. Threshold selection using Renyi′s entropy. Pattern Recognition. 1997; 30: 71–84. doi: 10.1016/S0031-3203(96)00065-9

6. Canny J. A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell. 1986; 6: 679–698. doi: 10.1109/TPAMI.1986.4767851

7. Shoujun Zhou, Yao Lu. Nana Li, Yuanquan Wang. Extension of the virtual electric field model using bilateral-like filter for active contours. Signal, Image and Video Processing. 9 March 2019. https://doi.org/10.1007/s11760-019-01456-x.

8. Tremeau A, Borel N. A region growing and merging algorithm to color segmentation[J]. Pattern Recognition. 1997; 30(7):1191–1203. doi: 10.1016/s0031-3203(96)00147-1

9. Sima H, Guo P, Zou Y, Wang Z, Xu M. Bottom-Up Merging Segmentation for Color Images With Complex Areas[J]. IEEE Transactions on Systems Man & Cybernetics Systems. 2018, 48:354–365. doi: 10.1109/TSMC.2016.2608831

10. Tan KS, Isa NAM. Color image segmentation using histogram thresholding—Fuzzy C-means hybrid approach. Pattern Recognition. 2011;44: 1–15. doi: 10.1016/j.patcog.2010.07.013

11. Tan KS, Isa NAM, Lim Wei Hong. Color image segmentation using adaptive unsupervised clustering approach. Applied Soft Computing. 2013; 13: 2017–2036. doi: 10.1016/j.asoc.2012.11.038

12. Yang A.Y, Wright J, Ma Y, Sastry SS. Unsupervised segmentation of natural images via lossy data compression[J]. Computer Vision & Image Understanding. 2008; 2: 212–225. doi: 10.1016/j.cviu.2007.07.005

13. Vargas Mújica, Funes Dante F.J.G., Rosalessilva A.J. A fuzzy clustering algorithm with spatial robust estimation constraint for noisy color image segmentation. Pattern Recognition Letters.2013;34:400–413. doi: 10.1016/j.patrec.2012.10.004

14. Liu H, Feng Z, Chaudhary V. Pareto-based interval type-2 fuzzy c-means with multi-scale JND color histogram for image segmentation[J]. Digital Signal Processing. 2018,76:75–83. doi: 10.1016/j.dsp.2018.02.005

15. Mignotte M. Segmentation by fusion of histogram-based k-means clusters in different color spaces. IEEE Transactions on Image Processing. 2008; 5: 780–787. doi: 10.1109/TIP.2008.920761 18390382

16. Dariusz Małyszko, Jarosław Stepaniuk. Adaptive Rough Entropy Clustering Algorithms in Image Segmentation. Fundamenta Informaticae. 2010; 98: 199–231. doi: 10.3233/FI-2010-224

17. Chen HP, Shen XJ, Long JW. Histogram-based colour image fuzzy clustering algorithm. Multimedia Tools & Applications. 2016; 18: 11417–11432. http://dx.doi.org/10.1007/s11042-015-2860-6.

18. Cheng HD, Jiang XH, Sun Y, Wang J. Color image segmentation: advances and prospects. Pattern Recognition. 2001; 12: 2259–2281. doi: 10.1016/s0031-3203(00)00149-7

19. Hou Z, Hu Q, Nowinski WL. On minimum variance thresholding[J]. Pattern Recognition Letters, 2006, 27:1732–1743. doi: 10.1016/j.patrec.2006.04.012

20. Li CH., Lee CK. Minimum cross entropy thresholding. Pattern Recognition. 1993; 26: 617–625. doi: 10.1016/0031-3203(93)90115-d

21. Pal NR. On minimum cross-entropy thresholding. Pattern Recognition. 1996; 4: 575–580. doi: 10.1016/0031-3203(95)00111-5

22. Malyszko Dariusz, Stepaniuk Jaroslaw. Adaptive multilevel rough entropy evolutionary thresholding. Information Sciences. 2010; 180: 1138–1158. doi: 10.1016/j.ins.2009.11.034

23. Otsu N. A threshold selection method for grey level histograms. IEEE Transactions on System, Man and Cybernetics. 1979;1: 62–66.

24. Albuquerque MPD, Esquef IA., Mello ARG., & Albuquerque MPD. Image thresholding using tsallis entropy. Pattern Recognition Letters. 2004; 9: 1059–1065. doi: 10.1016/j.patrec.2004.03.003

25. Yin PY. Multilevel minimum cross entropy threshold selection based on particle swarm optimization. Applied Mathematics & Computation. 2007;2: 503–513. doi: 10.1016/j.amc.2006.06.057

26. Sahoo PK, Arora G. A thresholding method based on two dimensional Renyi′s entropy. Pattern Recognition. 2004;37: 1149–1161. doi: 10.1016/j.patcog.2003.10.008

27. Sarkar Soham, Das Swagatam, Chaudhuri Sheli Sinha. A Multilevel color image thresholding scheme based on minimum cross entropy and differential evolution. Pattern Recognition Letters. 2015;54:27–35. doi: 10.1016/j.patrec.2014.11.009

28. Rosenfeld A, De la Torre P. Histogram concavity analysis as an aid in threshold selection. IEEE Transactions on Systems Man and Cybernetics. 1983;13: 231–235. doi: 10.1109/TSMC.1983.6313118

29. Lim YK, Lee SU. On the color image segmentation algorithm based on the thresholding and the fuzzy c-means techniques. Pattern Recognition. 1990; 23: 935–952. doi: 10.1016/0031-3203(90)90103-r

30. Mohabey A, Ray AK. Rough set theory based segmentation of color images. In:19th Internat. Conf. North Amer. Fuzzy Inform. Process. Soc.(NAIPS),338–342.

31. Mohabey A, Ray AK. Fusion of rough set theoretic approximations and FCM for color image segmentation. IEEE International Conference on Systems, Man, and Cybermetics. 2000;2: 1529–1534. http://dx.doi.org/10.1109/ICSMC.2000.886073.

32. Pawlak Z. Rough sets. International Journal of Computer and Information Sciences. 1982;5: 341–356. http://dx.doi.org/10.1007/BF01001956.

33. Mushrif MM, Ray AK. Color image segmentation: Rough-set theoretic approach. Pattern Recognition Letters. 2008;4: 483–493. http://dx.doi.org/10.1016/j.patrec.2007.10.026.

34. Xie CH, Liu YJ, Chang JY. Medical image segmentation using rough set and local polynomial regression. Multimedia Tools & Applications. 2015;6: 1885–1914. http://dx.doi.org/10.1007/s11042-013-1723-2.

35. Li M, Shang CX, Feng SZ, Fan JP. Quick attribute reduction in inconsistent decision tables. Information Sciences. 2014; 254: 155–180. http://dx.doi.org/10.1016/j.ins.2013.08.038.

36. Cheng HD, Jiang XH, Wang J. Color image segmentation based on homogram thresholding and region merging. Pattern Recognition. 2002; 35: 373–393. http://dx.doi.org/10.1016/S0031-3203(01)00054-1.

37. Liu J, Yang YH. Multiresolution Color Image Segmentation. IEEE Transactions on Pattern Analysis & Machine Intelligence. 1994; 7: 689–700. doi: 10.1109/34.297949

38. Borsotti Campadelli, Schettini. Quantitative evaluation of color image segmentation results. Pattern Recognition Letters. 1998;8: 741–747. http://dx.doi.org/10.1016/S0167-8655(98)00052-X

39. Unnikrishnan R, Pantofaru C, Hebert M. Toward Objective Evaluation of Image Segmentation Algorithms[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence. 2007; 29(6):929–944. http://dx.doi.org/10.1109/TPAMI.2007.1046.

40. M Meilă, Comparing clusterings: an axiomatic view, in Proceedings of the 22nd Int. Conf. on Machine Learning, ICML05, Bonn, 7–11 August 2005 (ACM,New York, 2005), pp. 577–584.

41. Freixenet J, Munoz X, Raba D, Marti J, Cuff X, Yet another survey on image segmentation: region and boundary information integration, in ECCV 2002,Copenhagen, 27 May—2 June 2002. Lecture Notes in Computer Science, 2352 (Springer, Berlin Heidelberg, 2002), pp. 408–422.

42. D Martin, C Fowlkes, D Tal, J Malik, A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics, in Proceedings of the 8th Int. Conf. Computer Vision, ICCV 2001, Vancouver, 7–14, July 2001, vol. 2 (IEEE, Piscataway, 2001), pp. 416–423.

43. Comanicu D., Meer P. Mean shift: a robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2002; 24: 603–619. doi: 10.1109/34.1000236

44. J. Shi, J. Malik, Normalized cuts and image segmentation, in: Proceedings of International Conference on Computer Vision and Pattern Recognition. 1997, pp. 731–737.

45. Felzenszwalb P., Huttenlocher D. Efficient graph-based image segmentation. International Journal on Computer Vision. 2004; 59 (2):167–181. http://dx.doi.org/10.1023/B:VISI.0000022288.19776.77.


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