Abstract
Although off-the-shelf clustering algorithms, such as those based on spectral graph theory, do a pretty good job at finding clusters of arbitrary shape and structure, they are inherently unable to satisfactorily deal with situations involving the presence of cluttered backgrounds. On the other hand, dominant sets, a generalization of the notion of maximal clique to edge-weighted graphs, exhibit a complementary nature: they are remarkably effective in dealing with background noise but tend to favor compact groups. In order to take the best of the two approaches, in this paper we propose to combine path-based similarity measures, which exploit connectedness information of the elements to be clustered, with the dominant-set approach. The resulting algorithm is shown to consistently outperform standard clustering methods over a variety of datasets under severe noise conditions.
Chapter PDF
References
Chang, H., Yeung, D.: Robust path-based spectral clustering. Pattern Recognition 41(1), 191–203 (2008)
Chehreghani, M.H.: Information-Theoretic Validation of Clustering Algorithms. Ph.D. thesis, ETH ZURICH (2013)
Fischer, B., Buhmann, J.M.: Bagging for path-based clustering. IEEE Trans. Pattern Anal. Mach. Intell. 25(11), 1411–1415 (2003a)
Fischer, B., Buhmann, J.M.: Path-based clustering for grouping of smooth curves and texture segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(4), 513–518 (2003b)
Fischer, B., Buhmann, J.M.: Path-based clustering for grouping of smooth curves and texture segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(4), 513–518 (2003c)
Lichman, M.: UCI machine learning repository (2013). http://archive.ics.uci.edu/ml
Ng, A.Y., Jordan, M.I., Weiss, Y.: On spectral clustering: analysis and an algorithm. In: Advances in Neural Information Processing Systems, pp. 849–856. MIT Press (2001)
Pavan, M., Pelillo, M.: Dominant sets and pairwise clustering. IEEE Trans. Pattern Anal. Machine Intell. 29(1), 167–172 (2007)
Pelillo, M.: What is a cluster? perspectives from game theory. In: Proc. of the NIPS Workshop on Clustering Theory (2009)
Rota Bulò, S., Pelillo, M.: A game-theoretic approach to hypergraph clustering. IEEE Trans. Pattern Anal. Machine Intell. 35(6), 1312–1327 (2013)
Rota Bulò, S., Pelillo, M., Bomze, I.M.: Graph-based quadratic optimization: A fast evolutionary approach. Computer Vision and Image Understanding 115(7), 984–995 (2011)
Rota Bulò, S., Torsello, A., Pelillo, M.: A game-theoretic approach to partial clique enumeration. Image Vision Comput. 27(7), 911–922 (2009)
Torsello, A., Rota Bulò, S., Pelillo, M.: Grouping with asymmetric affinities: a game-theoretic perspective. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), pp. 292–299 (2006)
Torsello, A., Rota Bulò, S., Pelillo, M.: Beyond partitions: allowing overlapping groups in pairwise clustering. In: 19th International Conference on Pattern Recognition (ICPR 2008), December 8–11, 2008, Tampa, Florida, USA, pp. 1–4 (2008)
Zelnik-manor, L., Perona, P.: Self-tuning spectral clustering. In: Advances in Neural Information Processing Systems 17, pp. 1601–1608. MIT Press (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Zemene, E., Pelillo, M. (2015). Path-Based Dominant-Set Clustering. In: Murino, V., Puppo, E. (eds) Image Analysis and Processing — ICIAP 2015. ICIAP 2015. Lecture Notes in Computer Science(), vol 9279. Springer, Cham. https://doi.org/10.1007/978-3-319-23231-7_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-23231-7_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-23230-0
Online ISBN: 978-3-319-23231-7
eBook Packages: Computer ScienceComputer Science (R0)