Outlier rejection fuzzy c-means (ORFCM) Segmentation

Outlier rejection fuzzy c-means (ORFCM) algorithm for image segmentation

This paper presents a fuzzy clustering-based technique for image segmentation. Many attempts have been put into practice to increase the conventional fuzzy c-means (FCM) performance. In this paper, the sensitivity of the soft membership function of the FCM algorithm to the outlier is considered and the new exponent operator on the Euclidean distance is implemented in the membership function to improve the outlier rejection characteristics of the FCM. The comparative quantitative and qualitative studies are performed among the conventional k-means (KM), moving KM, and FCM algorithms; the latest state-of-the-art clustering algorithms, namely the adaptive fuzzy moving KM , adaptive fuzzy KM, and new weighted FCM algorithms; and the proposed outlier rejection FCM (ORFCM) algorithm. It is revealed from the experimental results that the ORFCM algorithm outperforms the other clustering algorithms in various evaluation functions.

Anahtar Kelimeler:

Outlier rejection fuzzy c-means, fuzzy c-means, moving k-means, k-means, clustering, outlier

Outlier rejection fuzzy c-means (ORFCM) algorithm for image segmentation

Keywords:

Outlier rejection fuzzy c-means, fuzzy c-means, moving k-means, k-means, clustering, outlier,

PDF

___

A. Krishnan, C. Lewis, D. Day, “Vision system for identifying road signs using triangulation and bundle adjustment”, IEEE Conference on Intelligent Transportation Systems, pp. 36–41, 2009.
H. Ichihashi, A. Notsu, K. Honda, T. Katada, M. Fujiyoshi, “Vacant parking space detector for outdoor parking lot by using surveillance camera and FCM classifier”, IEEE International Conference on Fuzzy Systems, pp. 127–134, 200 E. Lee, W. Kang, S. Kim, J. Paik, “Color shift model-based image enhancement for digital multifocusing based on a multiple color-filter aperture camera”, IEEE Transactions on Consumer Electronics, Vol. 56, pp. 317–323, 2010. Z.Q. Liu, H.L. Liew, J.G. Clement, C.D.L. Thomas, “Bone image segmentation”, IEEE Transactions on Biomedical Engineering, Vol. 46, pp. 565–573, 1999.
X. Li, A. Abaza, D.E. Nassar, H. Ammar, “Fast and accurate segmentation of dental X-ray records”, Lecture Notes in Computer Science, Vol. 3832, pp. 688–696, 2005.
M. Hu, Q. Zhang, Z. Wang, G. Wu, “An improved fuzzy c means and Kathunen-Loeve transform method for face detection”, Proceedings of the 3rd International Conference on Innovative Computing Information and Control, pp. 201–204, 2008.
H. Gu, G.D. Su, C. Du, “Fuzzy and ISODATA classification of face contours”, Proceedings of the International Conference on Machine Learning and Cybernetics, pp. 3568–3573, 2004.
L. Ianming, Y. Xue, T. Yahagi, “A method of face recognition based on fuzzy c-means clustering and associated sub-NNs”, IEEE Transactions on Neural Networks, Vol. 18, pp. 150–160, 2007.
R. Gottumukkal, V.K. Asari, “Real time face detection from color video stream based on PCA method”, Proceedings of the 32nd Workshop on Applied Imagery Pattern Recognition, pp. 146–150, 2003.
M. Ceylan, Y. ¨ Ozbay, O.N. U¸ can, E. Yıldırım, “A novel method for lung segmentation on chest CT images: complexvalued artificial neural network with complex wavelet transform”, Turkish Journal of Electrical Engineering and Computer Sciences, Vol. 18, pp. 613–624, 2010.
B. Sahiner, N. Petrick, C.H. Ping, L.M. Hadjiiski, C. Paramagul, M.A. Helvie, M.N. Gurcanc, “Computer-aided characterization of mammographic masses: accuracy of mass segmentation and its effects on characterization”, IEEE Transactions on Medical Imaging, Vol. 20, pp. 1275–1284, 2001.
N.A.M. Isa, M.Y. Mashor, N.H. Othman, “Comparison of segmentation performance of clustering algorithms for pap smear images”, Proceedings of the International Conference on Robotics, Vision, Information and Signal processing, pp. 118–125, 2003.
S. Wang, M. Wang, “A new detection algorithm (NDA) based on fuzzy cellular neural networks for white blood cell detection”, IEEE Transactions on Information Technology in Biomedicine, Vol. 10, pp. 5–10, 2006.
J.B. MacQueen, “Some methods for classification and analysis of multivariate observations”, Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297, 1967.
J.C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, New York, Plenum Press, 1981.
M.Y. Mashor, “Hybrid training algorithm for RBF network”, International Journal of the Computer, the Internet and Management, Vol. 8, pp. 50–65, 2000.
N.A.M. Isa, A.S. Samy, U.K. Ngah, “Adaptive fuzzy moving k-means clustering algorithm for image segmentation”, IEEE Transactions on Consumer Electronics, Vol. 55, pp. 2145–2153, 2009.
F.U. Siddiqui, N.A.M. Isa, “Enhanced moving k-means (EMKM) algorithm for image segmentation”, IEEE Transactions on Consumer Electronics, Vol. 57, pp. 833–841, 2011.
P.R. Kersten, “Fuzzy order statistics and their application to fuzzy clustering”, IEEE Transactions on Fuzzy Systems, Vol. 7, pp. 708–712, 1999.
R. Hathaway, J.C. Bezdek, Y. Hu, “Generalized fuzzy c-means clustering strategies using L P norm distances”, IEEE Transactions on Fuzzy Systems, Vol. 8, pp. 576–582, 2002.
X. Wang, Y. Wang, L. Wang, “Improving fuzzy c-means clustering based on feature-weight learning”, Pattern Recognition Letters , Vol. 25, pp. 1123–1132, 2004.
W.L. Hung, M.S. Yang, D.H. Chen, “Bootstrapping approach to feature-weight selection in fuzzy c-means algorithms with an application in color image segmentation”, Pattern Recognition Letters, Vol. 29, pp. 1317–1325, 2008.
C.H. Li, W.C. Huang, B.C. Kuo, C.C. Hung, “A novel fuzzy weighted c-means method for image classification”, International Journal of Fuzzy Systems, Vol. 10, pp. 168–173, 2008.
C.C. Hung, S. Kulkarni, B.C. Kuo, “A new weighted fuzzy c-means clustering algorithm for remotely sensed image classification”, IEEE Journal of Selected Topics in Signal Processing, Vol. 5, pp. 543–553, 2011.
S.N. Sulaiman, N.A.M. Isa, “Adaptive fuzzy-k-means clustering algorithm for image segmentation”, IEEE Transactions on Consumer Electronics, Vol. 56, pp. 2661–2668, 2010.
B. Thomas, G. Raju, W. Sonam, “A modified fuzzy c-means algorithm for natural data exploration”, Proceedings of World Academy of Science: Engineering & Technology, Vol. 49, pp. 478–481, 2009.
X.L. Xie, G. Beni, “A validity measure for fuzzy clustering”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 13, pp. 841–847, 1991.