An Unsupervised Learning Approach to
Content-based Image Retrieval
Yixin Chen, James Z. Wang
The Pennsylvania State University, University Park, PA 16802
Robert Krovetz
NEC Corporation
Abstract:
''Semantic gap'' is an open challenging problem in content-based image
retrieval. It reflects the discrepancy between low-level imagery
features used by the ret rieval algorithm and high-level concepts
required by system users. This paper in troduces a novel image
retrieval scheme, CLUster-based rEtrieval of images by un supervised
learning (CLUE), to tackle the semantic gap problem. CLUE is built on
a hypothesis that {\it images of the same semantics tend to be
clustered}. It a ttempts to narrow the semantic gap by retrieving
image clusters based on not onl y the feature similarity of images to
the query, but also how images are similar to each other. CLUE has
been tested using examples from a database of about $60,000$
general-purpose images. Empirical results demonstrate the
effectiveness of CLUE.
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Citation:
Yixin Chen, James Z. Wang and Robert Krovetz, ``An Unsupervised
Learning Approach to Content-Based Image Retrieval,'' Proc. IEEE
International Symposium on Signal Processing and its Applications,
vol. 1, pp. 197-200, Paris, France, July 2003.
Copyright 2003 IEEE.
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Last Modified:
July 23 2003
© 2003