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.

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Last Modified: April 17 2001
2001