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Cited by 2,727

The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.

Statistical shape analysis: clustering, learning, and testing

Srivastava, A., Joshi, S.H., Mio, W., Xiuwen Liu·2005·IEEE Transactions on Pattern Analysis and Machine Intelligence
DOI 10.1109/tpami.2005.86
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Abstract

Using a differential-geometric treatment of planar shapes, we present tools for: 1) hierarchical clustering of imaged objects according to the shapes of their boundaries, 2) learning of probability models for clusters of shapes, and 3) testing of newly observed shapes under competing probability models. Clustering at any level of hierarchy is performed using a mimimum variance type criterion criterion and a Markov process. Statistical means of clusters provide shapes to be clustered at the next higher level, thus building a hierarchy of shapes. Using finite-dimensional approximations of spaces tangent to the shape space at sample means, we (implicitly) impose probability models on the shape space, and results are illustrated via random sampling and classification (hypothesis testing). Together, hierarchical clustering and hypothesis testing provide an efficient framework for shape retrieval. Examples are presented using shapes and images from ETH, Surrey, and AMCOM databases.

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Metadata

Authors
Srivastava, A., Joshi, S. H., Mio, W., & Liu, X.
Venue
IEEE Transactions on Pattern Analysis and Machine Intelligence
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2005-04
Volume
27
Issue
4
Pages
590-602
ISSN
0162-8828 (ISSN-L)
DOI
10.1109/tpami.2005.86
OpenAlex
W2146932984
Cited by
2,727
References
30
FWCI
232.15
Type
journal article
Language
en
Open access
closed

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