Manifold Clustering of Shapes

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	Similar Articles Articles by Dragomir Yankov Articles by Eamonn Keogh

Sixth IEEE International Conference on Data Mining (ICDM'06)

Hong Kong

December 18-December 22

ISBN: 0-7695-2701-9

	ASCII Text	x
	Dragomir Yankov, Eamonn Keogh, "Manifold Clustering of Shapes," Data Mining, IEEE International Conference on, pp. 1167-1171, Sixth IEEE International Conference on Data Mining (ICDM'06), 2006.

	BibTex	x
	@article{ 10.1109/ICDM.2006.101, author = {Dragomir Yankov and Eamonn Keogh}, title = {Manifold Clustering of Shapes}, journal ={Data Mining, IEEE International Conference on}, volume = {0}, year = {2006}, issn = {1550-4786}, pages = {1167-1171}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2006.101}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Data Mining, IEEE International Conference on TI - Manifold Clustering of Shapes SN - 1550-4786 SP1167 EP1171 A1 - Dragomir Yankov, A1 - Eamonn Keogh, PY - 2006 KW - null VL - 0 JA - Data Mining, IEEE International Conference on ER -

Dragomir Yankov, University of California, Riverside, USA

Eamonn Keogh, University of California, Riverside, USA

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2006.101

Shape clustering can significantly facilitate the automatic labeling of objects present in image collections. For example, it could outline the existing groups of pathological cells in a bank of cyto-images; the groups of species on photographs collected from certain aerials; or the groups of objects observed on surveillance scenes from an office building.

Here we demonstrate that a nonlinear projection algorithm such as Isomap can attract together shapes of similar objects, suggesting the existence of isometry between the shape space and a low dimensional nonlinear embedding. Whenever there is a relatively small amount of noise in the data, the projection forms compact, convex clusters that can easily be learned by a subsequent partitioning scheme. We further propose a modification of the Isomap projection based on the concept of degree-bounded minimum spanning trees. The proposed approach is demonstrated to move apart bridged clusters and to alleviate the effect of noise in the data.

Citation:

Dragomir Yankov, Eamonn Keogh, "Manifold Clustering of Shapes," icdm, pp.1167-1171, Sixth IEEE International Conference on Data Mining (ICDM'06), 2006

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