Rajpoot, Nasir M. (Nasir Mahmood) and Arif, Muhammad. (2008) Unsupervised shape clustering using diffusion map. Annals of the BMVA, Vol.2008 (No.5). pp. 1-17.

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Abstract

The quotient space of all smooth and connected curves represented by a fixed number of boundary points is a finite-dimensional Riemannian manifold, also known as a shape manifold. This makes the preservation of locality a critically important issue when reducing the dimensionality of shapes on the manifold. We present a completely unsupervised clustering algorithm employing diffusion maps for locality-preserving embedding of shapes onto a much lower-dimensional space. The algorithm first obtains a non-linear low-dimensional embedding of shape context features of outer boundary contours of the shapes. Considering the embedded coordinates as a new minimalist representation of shapes, a clustering of shapes is obtained using a finite mixture model. The proposed clustering algorithm is computationally efficient, as it relies on clustering in a very lowdimensional space, and produces much improved results (88.6% for a 7-class dataset) as compared to clustering with conventional linear projections.

Item Type:	Journal Article
Subjects:	Q Science > QA Mathematics Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Divisions:	Faculty of Science > Computer Science
Library of Congress Subject Headings (LCSH):	Riemannian manifolds, Algorithms
Journal or Publication Title:	Annals of the BMVA
Publisher:	The British Machine Vision Association and Society for Pattern Recognition
Date:	2008
Volume:	Vol.2008
Number:	No.5
Page Range:	pp. 1-17
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
URI:	http://wrap.warwick.ac.uk/id/eprint/37083

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