Abstract
To obtain the satisfying performance of noisy image segmentation, a multiobjective fuzzy clustering algorithm based on robust local spatial information (MFC_RLS) is proposed. In this method, the robust local spatial information derived from the image is introduced into fitness functions which utilize the fuzzy compactness and fuzzy separation among the clusters. In addition, after producing the set of non-dominated solutions, the final segmentation result is chosen by a validity index with the robust local spatial information. Experimental results show that MFC_RLS behaves well in segmenting noisy images.
This work is supported by the National Natural Science Foundation of China (Grant Nos. 61102095 and 61202153), the Natural Science Basic Research Plan in Shaanxi Province of China (Grant No. 2012JQ8045), the Scientific Research Program Funded by Shaanxi Provincial Education Department (Grant No. 11JK1008), and the Research Fund Program of Key Lab of Intelligent Perception and Image Understanding of Ministry of Education of China (Grant No. IPIU012011008).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Addison-Wesley, Massachusetts (1992)
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)
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)
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)
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)
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)
Handl, J., Knowles, J.: An evolutionary approach to multiobjective clustering. IEEE Transactions on Evolutionary Computation 11(1), 56–76 (2006)
Mukhopadhyay, A., Maulik, U.: A multiobjective approach to MR brain image segmentation. Applied Soft Computing 11(1), 872–880 (2011)
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)
Xie, X.L., Beni, G.: A validity measure for fuzzy clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 13, 841–847 (1991)
Pakhira, M., Bandyopadhyay, S., Maulik, U.: Validity index for crisp and fuzzy clusters. Pattern Recognition 37, 487–501 (2004)
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)
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)
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)
Wu, M., Scholkopf, B.: A local learning approach for clustering. In: Advances in Neural Information Processing Systems, pp. 1529–1536. MIT Press, USA (2007)
Yeung, K.Y., Ruzzo, W.L.: An empirical study on principal component analysis for clustering gene expression data. Bioinformatics 17(9), 763–774 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhao, F., Liu, H., Fan, J. (2013). A Multiobjective Fuzzy Clustering Algorithm Based on Robust Local Spatial Information for Image Segmentation. In: Sun, C., Fang, F., Zhou, ZH., Yang, W., Liu, ZY. (eds) Intelligence Science and Big Data Engineering. IScIDE 2013. Lecture Notes in Computer Science, vol 8261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42057-3_64
Download citation
DOI: https://doi.org/10.1007/978-3-642-42057-3_64
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-42056-6
Online ISBN: 978-3-642-42057-3
eBook Packages: Computer ScienceComputer Science (R0)