Skip to content Skip to navigation
University of Warwick
  • Study
  • |
  • Research
  • |
  • Business
  • |
  • Alumni
  • |
  • News
  • |
  • About

University of Warwick
Publications service & WRAP

Highlight your research

  • WRAP
    • Home
    • Search WRAP
    • Browse by Warwick Author
    • Browse WRAP by Year
    • Browse WRAP by Subject
    • Browse WRAP by Department
    • Browse WRAP by Funder
    • Browse Theses by Department
  • Publications Service
    • Home
    • Search Publications Service
    • Browse by Warwick Author
    • Browse Publications service by Year
    • Browse Publications service by Subject
    • Browse Publications service by Department
    • Browse Publications service by Funder
  • Help & Advice
University of Warwick

The Library

  • Login
  • Admin

Unsupervised shape clustering using diffusion map

Tools
- Tools
+ Tools

Rajpoot, Nasir M. (Nasir Mahmood) and Arif, Muhammad (2008) Unsupervised shape clustering using diffusion map. Annals of the BMVA, Volume 2008 (Number 5).

[img]
Preview
PDF
WRAP_Rajpoot_2008-0005.pdf - Published Version - Requires a PDF viewer.

Download (738Kb) | Preview

Request Changes to record.

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
Official Date: 2008
Dates:
DateEvent
2008Published
Volume: Volume 2008
Number: Number 5
Number of Pages: 17
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
Related URLs:
  • Open Access File

Request changes or add full text files to a record

Repository staff actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics

twitter

Email us: wrap@warwick.ac.uk
Contact Details
About Us