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Unsupervised shape clustering using diffusion map
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Rajpoot, Nasir M. (Nasir Mahmood) and Arif, Muhammad (2008) Unsupervised shape clustering using diffusion map. Annals of the BMVA, Volume 2008 (Number 5).
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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 | ||||
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| Subjects: | Q Science > QA Mathematics Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software |
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| 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: |
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| 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 | ||||
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