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.

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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.

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Last Modified: April 17 2001