A multiobjective fuzzy clustering algorithm based on robust local spatial information for image segmentation

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8261 LNCS, Issue , 2013, Pages 505-512

  Zhao, Feng ^a   Liu, Hanqiang ^b   Fan, Jiulun ^a  

^a XI AN UNIVERSITY OF POSTS AND TELECOMMUNICATIONS   (China)

^b SHAANXI NORMAL UNIVERSITY   (China)

Author keywords

Cluster validity measure; Image segmentation; Multiobjective fuzzy clustering; Robust local spatial information

Indexed keywords

BIG DATA; CLUSTERING ALGORITHMS; FUZZY CLUSTERING;

CLUSTER VALIDITY MEASURES; FITNESS FUNCTIONS; FUZZY COMPACTNESS; MULTI OBJECTIVE; NONDOMINATED SOLUTIONS; SEGMENTATION RESULTS; SPATIAL INFORMATIONS; VALIDITY INDEX;

IMAGE SEGMENTATION;

EID: 84892877985     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-42057-3_64     Document Type: Conference Paper

References (16)

1. Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Addison-Wesley, Massachusetts (1992)
2. Chen, S.C., Zhang, D.Q.: Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure. IEEE Transaction on System, Man, and Cybernetics, Part B: Cybernetics 34(4), 1907-1916 (2004)
3. Ahmed, M.N., Yamany, S.M., Mohamed, N., Farag, A.A., Moriarty, T.: A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data. IEEE Transaction on Medical Imaging 21(3), 193-199 (2002)
4. Szilagyi, L., Benyo, Z., Szilagyii, S., Adam, H.S.: MR brain image segmentation using an enhanced fuzzy C-means algorithm. In: Proceedings of 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 1, pp. 724-726. IEEE Press, Cancun (2003)
5. Cai, W., Chen, S., Zhang, D.: Fast and robust fuzzy c-means clustering algorithms incorpo-rating local information for image segmentation. Pattern Recognition 40(3), 825-838 (2007)
6. Zhao, F., Jiao, L.C., Liu, H.Q.: Fuzzy c-means clustering with non local spatial information for noisy image segmentation. Frontiers of Computer Science in China 5, 45-56 (2011)
7. Handl, J., Knowles, J.: An evolutionary approach to multiobjective clustering. IEEE Transactions on Evolutionary Computation 11(1), 56-76 (2006)
8. Mukhopadhyay, A., Maulik, U.: A multiobjective approach to MR brain image segmentation. Applied Soft Computing 11(1), 872-880 (2011)
9. Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)
10. Xie, X.L., Beni, G.: A validity measure for fuzzy clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 13, 841-847 (1991)
11. Pakhira, M., Bandyopadhyay, S., Maulik, U.: Validity index for crisp and fuzzy clusters. Pattern Recognition 37, 487-501 (2004)
12. Deb, K., Agrawal, S., Pratab, A., Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: Deb, K., Rudolph, G., Lutton, E., Merelo, J.J., Schoenauer, M., Schwefel, H.-P., Yao, X. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 849-858. Springer, Heidelberg (2000)
13. Maulik, U., Bandyopadhyay, S.: Performance evaluation of some clustering algorithms and validity indices. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(12), 1650-1654 (2002)
14. Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceeding of the 8th International Conference of Computer Vision, vol. 2, pp. 416-423 (2001)
15. Wu, M., Scholkopf, B.: A local learning approach for clustering. In: Advances in Neural Information Processing Systems, pp. 1529-1536. MIT Press, USA (2007)
16. Yeung, K.Y., Ruzzo, W.L.: An empirical study on principal component analysis for clustering gene expression data. Bioinformatics 17(9), 763-774 (2001)