Towards parameter-independent data clustering and image segmentation

Pattern Recognition

Volumn 60, Issue , 2016, Pages 25-36

  Hou, Jian ^a   Liu, Weixue ^a   E, Xu ^a   Cui, Hongxia ^a  

^a BOHAI UNIVERSITY   (China)

Author keywords

Clustering; Dominant sets; Image segmentation; Similarity matrix; Similarity measure

Indexed keywords

CLUSTER ANALYSIS; IMAGE ENHANCEMENT; IMAGE SEGMENTATION; MATRIX ALGEBRA;

CLUSTERING; DOMINANT SET; HISTOGRAM EQUALIZATIONS; NUMBER OF CLUSTERS; PARAMETER DEPENDENTS; SIMILARITY MATRIX; SIMILARITY MEASURE; STATE-OF-THE-ART ALGORITHMS;

CLUSTERING ALGORITHMS;

EID: 84994810259     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2016.04.015     Document Type: Article

References (38)

1. [1] M. Ester, H.P. Kriegel, J. Sander, X.W. Xu, A density-based algorithm for discovering clusters in large spatial databases with noise, in: International Conference on Knowledge Discovery and Data Mining, 1996, pp. 226–231.
2. [2] R. Agrawal, J. Gehrke, D. Gunopulos, P. Raghavan, Automatic subspace clustering of high dimensional data, in: International Conference on Knowledge Discovery and Data Mining, 2005, pp. 517–521.
3. [3] Shi, J., Malik, J., Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22:8 (2000), 167–172.
4. [4] Brendan, J.F., Delbert, D., Clustering by passing messages between data points. Science 315 (2007), 972–976.
5. [5] Rodriguez, A., Laio, A., Clustering by fast search and find of density peaks. Science 344 (2014), 1492–1496.
6. [6] L.O. Hall, A. Chakeri, Relational data partitioning using evolutionary game theory, in: 2014 IEEE Symposium on Computational Intelligence and Data Mining, 2014, pp. 113–120.
7. [7] X. Zhu, C.C. Loy, S. Gong, Constructing robust affinity graphs for spectral clustering, in: IEEE International Conference on Computer Vision and Pattern Recognition, 2014, pp. 1450–1457.
8. [8] Gong, M., Liang, Y., Shi, J., Ma, W., Ma, J., Fuzzy c-means clustering with local information and kernel metric for image segmentation. IEEE Trans. Image Process. 22:2 (2013), 573–584.
9. [9] C. Couprie, L. Grady, L. Najman, H. Talbot, Power watersheds: a new image segmentation framework extending graph cuts, random walker and optimal spanning forest, in: IEEE International Conference on Computer Vision, 2009, pp. 731–738.
10. [10] Panagiotakis, C., Papadakis, H., Grinias, E., Komodakis, N., Fragopoulou, P., Tziritas, G., Interactive image segmentation based on synthetic graph coordinates. Pattern Recognit. 46:11 (2013), 2940–2952.
11. [11] Y. Zhao, X. Nie, Y. Duan, Y. Huang, S. Luo, A benchmark for interactive image segmentation algorithms, in: IEEE Workshop on Person-Oriented Vision, 2011, pp. 33–38.
12. [12] Wang, X., Tang, Y., Masnou, S., Chen, L., A global/local affinity graph for image segmentation. IEEE Trans. Image Process. 24:4 (2015), 1399–1411.
13. [13] A. Chakeri, L.O. Hall, Large data clustering using quadratic programming: a comprehensive quantitative analysis, in: 2015 IEEE International Conference on Data Mining Workshop, 2015, pp. 806–813.
14. [14] Fraley, C., Raftery, A.E., How many clusters? Which clustering method? Answers via model-based cluster analysis. Comput. J. 41:8 (1998), 578–588.
15. [15] Tibshirani, R., Walther, G., Hastie, T., Estimating the number of clusters in a data set via the gap statistic. J. R. Stat. Soc.—Ser. B: Stat. Methodol. 63:2 (2001), 411–423.
16. [16] Monti, S., Tamayo, P., Mesirov, J., Golub, T., Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data. Mach. Learn. 52:1–2 (2003), 91–118.
17. [17] Evanno, G., Regnaut, S., Goudet, J., Detecting the number of clusters of individuals using the software structure: a simulation study. Mol. Ecol. 14:8 (2005), 2611–2620.
18. [18] Bansal, N., Blum, A., Chawla, S., Correlation clustering. Mach. Learn. 56:1–3 (2004), 89–113.
19. [19] Demaine, E.D., Emanuel, D., Fiat, A., Immorlica, N., Correlation clustering in general weighted graphs. Theor. Comput. Sci. 361 (2006), 172–187.
20. [20] M. Pavan, M. Pelillo, A graph-theoretic approach to clustering and segmentation, in: IEEE International Conference on Computer Vision and Pattern Recognition, 2003, pp. 145–152.
21. [21] Pavan, M., Pelillo, M., Dominant sets and pairwise clustering. IEEE Trans. Pattern Anal. Mach. Intell. 29:1 (2007), 167–172.
22. [22] A. Torsello, S.R. Bulo, M. Pelillo, Beyond partitions: allowing overlapping groups in pairwise clustering, in: International Conference on Pattern Recognition, 2008, pp. 1–4.
23. [23] Hamid, R., Maddi, S., Johnson, A.Y., Bobick, A.F., Essa, I.A., Isbell, C., A novel sequence representation for unsupervised analysis of human activities. Artif. Intell. 173 (2009), 1221–1244.
24. [24] Frommlet, F., Tag snp selection based on clustering according to dominant sets found using replicator dynamics. Adv. Data Anal. Classif. 4 (2010), 65–83.
25. [25] Yang, X.W., Liu, H.R., Laecki, L.J., Contour-based object detection as dominant set computation. Pattern Recognit. 45 (2012), 1927–1936.
26. [26] Hou, J., Pelillo, M., A simple feature combination method based on dominant sets. Pattern Recognition 46:11 (2013), 3129–3139.
27. [27] Hou, J., Xia, Q., Qi, N., Experimental study on dominant sets clustering. IET Comput. Vis. 9:2 (2015), 208–215.
28. [28] J. Hou, X.E., L. Chi, Q. Xia, N. M. Qi, Dset++: a robust clustering algorithm, in: International Conference on Image Processing, 2013, pp. 3795–3799.
29. [29] A. Torsello, S.R. Bulo, M. Pelillo, Grouping with asymmetric affinities: a game-theoretic perspective, in: IEEE International Conference on Computer Vision and Pattern Recognition, vol. 1, 2006, pp. 292–299.
30. [30] Bulo, S.R., Pelillo, M., Bomze, I.M., Graph-based quadratic optimization: a fast evolutionary approach. Comput. Vis. Image Underst. 115:7 (2011), 984–995.
31. [31] Gionis, A., Mannila, H., Tsaparas, P., Clustering aggregation. ACM Trans. Knowl. Discov. Data 1:1 (2007), 1–30.
32. [32] Zahn, C.T., Graph-theoretical methods for detecting and describing gestalt clusters. IEEE Trans. Comput. 20:1 (1971), 68–86.
33. [33] Chang, H., Yeung, D.Y., Robust path-based spectral clustering. Pattern Recognit. 41:1 (2008), 191–203.
34. [34] Veenman, C.J., Reinders, M., Backer, E., A maximum variance cluster algorithm. IEEE Trans. Pattern Anal. Mach. Intell. 24:9 (2002), 1273–1280.
35. [35] Fu, L., Medico, E., Flame, a novel fuzzy clustering method for the analysis of dna microarray data. BMC Bioinform. 8:1 (2007), 1–17.
36. [36] Acharya, T., Ray, A.K., Image Processing: Principles and Applications. 2005, Wiley-Interscience, Hoboken, New Jersey.
37. [37] Daszykowski, M., Walczak, B., Massart, D.L., Looking for natural patterns in data: Part 1. Density-based approach. Chem. Intell. Lab. Syst. 56:2 (2001), 83–92.
38. [38] J. Hou, X.E., L. Chi, Q. Xia, N.M. Qi, Robust clustering based on dominant sets, in: International Conference on Pattern Recognition, 2014, pp. 1466–1471.