Content-Based Image Retrieval by Clustering
Yixin Chen
University of New Orleans, New Orleans, LA 70148
James Z. Wang
The Pennsylvania State University, University Park, PA 16802
Robert Krovetz
Teoma Technologies, Piscataway, NJ 08554
Abstract:
In a typical content-based image retrieval (CBIR) system, query
results are a set of images sorted by feature similarities with
respect to the query. However, images with high feature similarities
to the query may be very different from the query in terms of
semantics. This is known as the semantic gap. We introduce a novel
image retrieval scheme, CLUster-based rEtrieval of images by
unsupervised learning (CLUE), which tackles the semantic gap problem
based on a hypothesis: semantically similar images tend to be
clustered in some feature space. CLUE attempts to capture semantic
concepts by learning the way that images of the same semantics are
similar and retrieving image clusters instead of a set of ordered
images. Clustering in CLUE is dynamic. In particular, clusters formed
depend on which images are retrieved in response to the
query. Therefore, the clusters give the algorithm as well as the users
semantic relevant clues as to where to navigate. CLUE is a general
approach that can be combined with any real-valued symmetric
similarity measure (metric or nonmetric). Thus it may be embedded in
many current CBIR systems. Experimental results based on a database of
about 60,000 images from COREL demonstrate improved performance.
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Citation:
Yixin Chen, James Z. Wang and Robert Krovetz, ``Content-Based Image
Retrieval by Clustering,'' Proc. 5th International Workshop on
Multimedia Information Retrieval, in conjunction with ACM Multimedia,
pp. 193-200, Berkeley, CA, ACM, November 2003.
Copyright 2003 ACM.
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Last Modified:
September 7, 2003
© 2003