A fuzzy clustering algorithm with spatial robust estimation constraint for noisy color image segmentation

Pattern Recognition Letters

Volumn 34, Issue 4, 2013, Pages 400-413

  Mújica Vargas, Dante ^a   Gallegos Funes, Francisco J ^a   Rosales Silva, Alberto J ^a  

^a INSTITUTO POLITÉCNICO NACIONAL   (Mexico)

Author keywords

Color images; Fuzzy C Means; L estimator; Noise; RM L estimator; Segmentation

Indexed keywords

COLOR; COLOR IMAGE PROCESSING; FUZZY CLUSTERING; FUZZY SYSTEMS; IMAGE RECONSTRUCTION; IMAGE SEGMENTATION; REMOTE SENSING;

COLOR IMAGE SEGMENTATION; COLOR IMAGES; FUZZY C MEAN; FUZZY C-MEANS CLUSTERING ALGORITHMS; L-ESTIMATOR; NOISE; REMOTE SENSING IMAGES; SPATIAL INFORMATIONS;

CLUSTERING ALGORITHMS;

EID: 84881152060     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2012.10.004     Document Type: Article

References (30)

1. Sowmya, B., Sheela Rani, B., 2011. Colour image segmentation using fuzzy clustering techniques and competitive neural network. Appl. Soft Comput. 11 (3), 3170-3178.
2. Liu, H.Q., Jiao, L.C., Zhao, F., 2010. Non-local spatial spectral clustering for image segmentation. Neurocomputing 74, 461-471.
3. Lukac, R., Plataniotis, K., 2007. Color Image Processing: Methods and Applications. CRC Press.
4. Abdel-Mottaleb, M., Ekgammal, A, 1999. Face detection in complex environments from color images. In: Proc. IEEE ICIP (International Conference of Image Processing) pp. 622-626. (Pubitemid 30550120)
5. Bezdek, J.C., 1981. Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York.
6. Cai, W., Chen, S., Zhang, D., 2007. Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentatio. Pattern Recognition 40, 825-838. (Pubitemid 44667762)
7. Zhao, F., Jiao, L, Liu, H., Gao, X., 2011. A novel fuzzy clustering algorithm with non local adaptive spatial constraint for image segmentation. Signal Processing 91, 988-999.
8. Xiang-Yang, W., Juan, B., 1173. A fast and robust image segmentation using FCM with spatial information. Digital Signal Process. 20, 1173-1182.
9. Gallegos-Funes, F.J., Ponomaryov, V., 2004. Real-time image filtering scheme based on robust estimators in presence of noise impulsive. Real Time Imaging 8 (2), 78-90.
10. Gallegos-Funes, F., Jose Varela-Benitez, Ponomaryov, V., 2008. Rank M-Type L (RM L)-Filter for Image Denoising In: IEICE Trans. Fundamentals of Electronics, Communications and Computer Sciences, E91-A (12), pp. 3817-3819.
11. Gallegos-Funes, F.J., Rosales-Silva, A.J., Toledo-Lopez, A., 2012. Multichannel image processing by using the rank M-type L-filter. J. Vis. Commun. Image Represent. 23 (2), 323-330.
12. Mújica-Vargas, D., Gallegos-Funes, F.J., Cruz-Santiago, R, 2011. Segmentation of noisy images using the rank M-type L-filter and the fuzzy C-means clustering algorithm, Lecture Notes in Computer Science. Vol. 6718, Pattern Recognition MCPR 2011, pp. 184-193.
13. Pǎtraşcu, V., 2007. Fuzzy image segmentation based on triangular function and its n-dimensional extension. Studies in Fuzziness and Soft Computing 210, 187-207.
14. Pitas, I., Venetsanopoulos, A.N., 1990. Nonlinear Digital Filters. Kluwer Academic Publishers, Boston.
15. Astola, J., Kousmanen, P., 1997. Fundamentals of Nonlinear Digital Filtering. CRC Press, Boca Raton-New York.
16. Hampel, F.R., Ronchetti, E.M., Rouseew, P.J., Stahel, W.A., 1986. Robust Statistics. The approach based on influence function. Wiley, New York.
17. Aizenberg, L., Astola, J., Breging, T., Butakoff, C, Egiazarian, K., Paily, D., 2003. Detectors of the impulsive noise and new effective filters for the impulse noise reduction. In: Proceedings of SPIE Image Processing, Algorithms and Systems II. Vol. 5014, pp. 410-428.
18. Valente de Oliveira, J., Pedrycz, W., 2007. Advances in Fuzzy Clustering and its Applications. John Wiley & Sons, Ltd.
19. Le Capitaine, H., Frélicot, C, 2011. A fast fuzzy c-means algorithm for color image segmentation. Laboratoire Mathémathiques, Image et Applications, Université de La Rochelle, France.
20. Angulo, J., Sierra, J., 2003. Color Segmentation by ordered mergins. In: IEEE International Conference on Image Processing.
21. Mather, P., Koch, M., 2011. Computer Processing of Remotely-Sensed Images: An Introduction, fourth ed. Wiley-Blackwell.
22. Okeke, F., Karnieli, A., 2006. Methods for fuzzy classification and accuracy assessment of historical aerial photographs for vegetation change analyses. Part I: Algorithm development. Int. J. Remote Sens. 27 (1-2), 153-176. (Pubitemid 43878381)
23. Liu, Z., Song, Y., Chen, J., Xie, C, Zhu, F., 2012. Color image segmentation using nonparametric mixture models with multivariate orthogonal polynomials. Neural Comput. Appl. 22, 801-811.
24. Yu, Z., Au, O., Zou, R., Yu, W., Tian, J., 2010. An adaptive unsupervised approach toward pixel clustering and color image segmentation. Pattern Recognition 43, 1889-1906.
25. K. Siang, N. Ashidi Mat Isa. "Color image segmentation using histogram thresholding - Fuzzy C-means hybrid approach,". Pattern Recognition 44, pp. 1-15, (2011).
26. Chatzis, S.P., Varvarigou, TA, 2008. A fuzzy clustering approach toward hidden Markov random field models for enhanced spatially constrained image segmentation. IEEE Trans. Fuzzy Syst. 16 (5), 1351-1361.
27. Wangenheim, A., Bertoldi, R., Abdala, D., Sobieranski, A., Coser, L, Jiang, X., Richter, M., Priese, L., Schmitt, F., 2009. Color image segmentation using an enhanced Gradient Network Method. Pattern Recognition Lett. 30, 1404-1412.
28. Khan, J., Adhami, R., Bhuiyan, S., Sobieranski, A., 2009. A customized Gabor filter for unsupervised color image segmentation. Image Vision Comput. 27, 489-501.
29. *b color space. Pattern Recognition Lett. 32, 891-902.
30. Fan, J., Han, M., Wang, J., 2009. Single point iterative weighted fuzzy C-means clustering algorithm for remote sensing image segmentation. Pattern Recognition 42, 2527-2540.