Scalable Integrated Region-based Image Retrieval
using IRM and Statistical Clustering
James Z. Wang, Yanping Du
The Pennsylvania State University
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.
Full Paper in Color
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,
Copyright 2001 ACM.
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April 17 2001