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Published Articles >> Table of Contents >> Abstract
International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 2
p. 18
Summarizing Inter-Query Learning in Content-Based Image Retrieval via Incremental Semantic Clustering
Iker Gondra, Oklahoma State University, Stillwater
Douglas R. Heisterkamp, Oklahoma State University, Stillwater
Full Article Text:
  
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ITCC.2004.1286583
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| Abstract |
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In previous work, we developed a novel Relevance Feedback
(RF) framework that learns One-class Support Vector
Machines (1SVM) from retrieval experience to represent the
set memberships of users' high level semantics. By doing a
fuzzy classification of a query into the regions of support
represented by the 1SVMs, past experience is merged with
short-term (i.e., intra-query) learning. However, this led
to the representation of long-term (i.e., inter-query) learning
with a constantly growing number of 1SVMs in the feature
space. We present an improved version of our earlier
work that uses an incremental k-means algorithm to cluster
1SVMs. The main advantage of the improved approach is
that it is scalable and can accelerate query processing by
considering only a small number of cluster representatives,
rather than the entire set of accumulated 1SVMs. Experimental
results against real data sets demonstrate the effectiveness
of the proposed method.
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 Additional Information
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Citation:
Iker Gondra, Douglas R. Heisterkamp,
"Summarizing Inter-Query Learning in Content-Based Image Retrieval via Incremental Semantic Clustering,"
itcc,
p. 18,
International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 2,
2004
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