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Scalable Integrated Region-based Image Retrieval using IRM and Statistical Clustering (2001)
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| Citations: | 19 - 0 self |
BibTeX
@MISC{Wang01scalableintegrated,
author = {James Z. Wang and Yanping Du},
title = {Scalable Integrated Region-based Image Retrieval using IRM and Statistical Clustering},
year = {2001}
}
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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 similaritybetween 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 inuence 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.
Keyphrases
statistical clustering region-based image retrieval inaccurate segmentation region feature individual region region-based image retrieval system scalable image retrieval system pathology slide overall similaritybetween image particular region general-purpose image large-scale image database original system simple querying interface overall similarity approach scalable algorithm region matching experimental simplicity image retrieval system region-matching scheme region segmentation matching time
