Scalable Integrated Region-based Image Retrieval
using IRM and Statistical Clustering
James Z. Wang, Yanping Du
The Pennsylvania State University
Abstract:
Statistical clustering is critical in designing scalable image
retrieval systems. In this paper, we present a scalable algorithm for
indexing and retrieving images based on region segmentation. The
method uses statistical clustering on region features and IRM
(Integrated Region Matching), a measure developed to evaluate overall
similarity between images that incorporates properties of all the
regions in the images by a region-matching scheme. Compared with
retrieval based on individual regions, our overall similarity approach
(a) reduces the influence of inaccurate segmentation, (b) helps to
clarify the semantics of a particular region, and (c) enables a simple
querying interface for region-based image retrieval systems. The
algorithm has been implemented as a part of our experimental
SIMPLIcity image retrieval system and tested on large-scale image
databases of both general-purpose images and pathology slides.
Experiments have demonstrated that this technique maintains the
accuracy and robustness of the original system while reducing the
matching time significantly.
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Citation:
James Z. Wang and Yanping Du, ``Scalable Integrated Region-Based Image
Retrieval Using IRM and Statistical Clustering,'' Proc. ACM and IEEE
Joint Conference on Digital Libraries, pp. 268-277, Roanoke, VA, ACM,
June 2001.
Copyright 2001 ACM.
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Last Modified:
April 17 2001
© 2001