Mean shift-based clustering

Pattern Recognition

Volumn 40, Issue 11, 2007, Pages 3035-3052

  Wu, Kuo Lung ^a   Yang, Miin Shen ^b  

^a KUN SHAN UNIVERSITY   (Taiwan)

^b CHUNG YUAN CHRISTIAN UNIVERSITY   (Taiwan)

Author keywords

Bandwidth selection; Generalized Epanechnikov kernel; Kernel functions; Mean shift; Mountain method; Noise; Parameter estimation; Robust clustering

Indexed keywords

BANDWIDTH; COMPUTATIONAL COMPLEXITY; CORRELATION METHODS; GRAPH THEORY; PARAMETER ESTIMATION; PROBLEM SOLVING;

BANDWIDTH SELECTION; GENERALIZED EPANECHNIKOV KERNEL; KERNEL FUNCTIONS; MEAN SHIFT; MOUNTAIN METHOD; ROBUST CLUSTERING;

CLUSTERING ALGORITHMS;

EID: 34347231626     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2007.02.006     Document Type: Article

References (44)

1. Vapnik V.N. Statistical Learning Theory (1998), Wiley, New York
2. Cristianini N., and Shawe-Taylor J. An Introduction to Support Vector Machines (2000), Cambridge University Press, Cambridge
3. Camastra F., and Verri A. A novel kernel method for clustering. IEEE Trans. Pattern Anal. Mach. Intell. 27 (2005) 801-805
4. Girolami M. Mercer kernel based clustering in feature space. IEEE Trans. Neural Networks 13 (2002) 780-784
5. Muller K.R., Mika S., Rätsch G., Tsuda K., and Schölkopf B. An introduction to kernel-based learning algorithm. IEEE Trans. Neural Networks 12 (2001) 181-202
6. Silverman B.W. Density Estimation for Statistics and Data Analysis (1998), Chapman & Hall, New York
7. Fukunaga K., and Hostetler L.D. The estimation of the gradient of a density function with applications in pattern recognition. IEEE Trans. Inf. Theory 21 (1975) 32-40
8. Cheng Y. Mean shift, mode seeking, and clustering. IEEE Trans. Pattern Anal. Mach. Intell. 17 (1995) 790-799
9. Yager R.R., and Filev D.P. Approximate clustering via the mountain method. IEEE Trans. Syst. Man Cybern. 24 (1994) 1279-1284
10. Comaniciu D. An algorithm for data-driven bandwidth selection. IEEE Trans. Pattern Anal. Mach. Intell. 25 (2003) 281-288
11. Comaniciu D., and Meer P. Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24 (2002) 603-619
12. Kim K.I., Jung K., and Kim J.H. Texture-based approach for text detection in images using support vector machines and continuously adaptive mean shift algorithm. IEEE Trans. Pattern Anal. Mach. Intell. 25 (2003) 1631-1639
13. Yang X., and Liu J. Unsupervised texture segmentation with one-step mean shift and boundary Markov random fields. Pattern Recognition Lett. 22 (2001) 1073-1081
14. Comaniciu D., Ramesh V., and Meer P. Kernel-based object tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25 (2003) 564-577
15. Peng N.S., Yang J., and Liu Z. Mean shift blob tracking with kernel histogram filtering and hypothesis testing. Pattern Recognition Lett. 26 (2005) 605-614
16. Debeir O., Ham P.V., Kiss R., and Decaestecker C. Tracking of migrating cells nuder phase-contrast video microscopy with combined mean-shift processes. IEEE Trans. Med. Imaging 24 (2005) 697-711
17. Chen H., and Meer P. Robust fusion of uncertain information. IEEE Trans. Syst. Man Cybern. B Cybern. 35 (2005) 578-586
18. Fashing M., and Tomasi C. Mean shift is a bound optimization. IEEE Trans. Pattern Anal. Mach. Intell. 27 (2005) 471-474
19. Chiu S.L. Fuzzy model identification based on cluster estimation. J. Intell. Fuzzy Syst. 2 (1994) 267-278
20. Yang M.S., and Wu K.L. A modified mountain clustering algorithm. Pattern Anal. Appl. 8 (2005) 125-138
21. Pal N.R., and Chakraborty D. Mountain and subtractive clustering method: improvements and generalizations. Int. J. Intell. Syst. 15 (2000) 329-341
22. Velthuizen B.P., Hall L.O., Clarke L.P., and Silbiger M.L. An investigation of mountain method clustering for large data sets. Pattern Recognition 30 (1997) 1121-1135
23. Fukunaga K. Introduction to Statistical Pattern Recognition (1990), Academic Press, New York
24. Beni G., and Liu X. A least biased fuzzy clustering method. IEEE Trans. Pattern Anal. Mach. Intell. 16 (1994) 954-960
25. Huber P.J. Robust Statistics (1981), Wiley, New York
26. Zhuang X., Wang T., and Zhang P. A highly robust estimator through partially likelihood function modeling and its application in computer vision. IEEE Trans. Pattern Anal. Mach. Intell. 14 (1992) 19-35
27. Wu K.L., and Yang M.S. Alternative c-means clustering algorithms. Pattern Recognition 35 (2002) 2267-2278
28. Stewart C.V. Minpran: a new robust estimator for computer vision. IEEE Trans. Pattern Anal. Mach. Intell. 17 (1995) 925-938
29. Dave R.N., and Krishnapuram R. Robust clustering methods: a unified view. IEEE Trans. Fuzzy Syst. 5 (1997) 270-293
30. Frigui H., and Krishnapuram R. A robust competitive clustering algorithm with applications in computer vision. IEEE Trans. Pattern Anal. Mach. Intell. 21 (1999) 450-465
31. Rose K., Gurewitz E., and Fox G.C. Statistical mechanics and phase transitions in clustering. Phys. Rev. Lett. 65 (1990) 945-948
32. Rose K., Gurewitz E., and Fox G.C. A deterministic annealing approach to clustering. Pattern Recognition Lett. 11 (1990) 589-594
33. Chen S., and Zhang D. Robust image segmentation using FCM with special constraints based on new kernel-induced distance measure. IEEE Trans. Syst. Man Cybern. B Cybern. 34 (2004) 1907-1916
34. Ahmed M.N., Yamany S.M., Mohamed N., Farag A.A., and Moriarty T. A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data. IEEE Trans. Med. Imaging 21 (2002) 193-199
35. Yang M.S., and Wu K.L. A similarity-based robust clustering method. IEEE Trans. Pattern Anal. Mach. Intell. 26 (2004) 434-448
36. Krishnapuram R., and Keller J.M. A possibilistic approach to clustering. IEEE Trans. Fuzzy Syst. 1 (1993) 98-110
37. Krishnapuram R., Frigui H., and Nasraoui O. Fuzzy and possibilistic shell clustering algorithm and their application to boundary detection and surface approximation. IEEE Trans. Fuzzy Syst. 3 (1995) 29-60
38. Frigui H., and Krishnapuram R. Clustering by competitive agglomeration. Pattern Recognition 30 (1997) 1223-1232
39. Bezdek J.C. Pattern Recognition with Fuzzy Objective Function Algorithm (1981), Plenum Press, New York
40. Bezdek J.C. Cluster validity with fuzzy sets. J. Cybern. 3 (1974) 58-73
41. Fukuyama Y., and Sugeno M. A new method of choosing the number of clusters for fuzzy c-means method. Proceedings of the 5th Fuzzy System Symposium (1989) 247-250 (in Japanese)
42. Xie X.L., and Beni G. A validity measure for fuzzy clustering. IEEE Trans. Pattern Anal. Mach. Intell. 13 (1991) 841-847
43. Wu K.L., and Yang M.S. A cluster validity index for fuzzy clustering. Pattern Recognition Lett. 26 (2005) 1275-1291
44. C.L. Blake, C.J. Merz, UCI repository of machine learning databases, a huge collection of artificial and real-world data sets, 1998. Available from: 〈http://www.ics.uci.edu∼mlearn/MLRepository.html〉.