NDLI: In-Plane Rotation and Scale Invariant Clustering Using Dictionaries

Content Provider	IEEE Xplore Digital Library
Author	Yi-Chen Chen Sastry, C.S. Patel, V.M. Phillips, P.J. Chellappa, R.
Copyright Year	1992
Abstract	In this paper, we present an approach that simultaneously clusters images and learns dictionaries from the clusters. The method learns dictionaries and clusters images in the radon transform domain. The main feature of the proposed approach is that it provides both in-plane rotation and scale invariant clustering, which is useful in numerous applications, including content-based image retrieval (CBIR). We demonstrate the effectiveness of our rotation and scale invariant clustering method on a series of CBIR experiments. Experiments are performed on the Smithsonian isolated leaf, Kimia shape, and Brodatz texture datasets. Our method provides both good retrieval performance and greater robustness compared to standard Gabor-based and three state-of-the-art shape-based methods that have similar objectives.
Sponsorship	IEEE Signal Processing Society
Page Count	15
File Size	6311993
Starting Page	2166
Ending Page	2180
File Format	PDF
ISSN	10577149
Volume Number	22
Issue Number	6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Dictionaries Transforms Shape Feature extraction Clustering algorithms Vectors Zirconium scale invariance Clustering content-based image retrieval (CBIR) dictionary learning radon transform rotation invariance
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design Software

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