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Scalable Integrated Region-Based Image Retrieval Using IRM and Statistical Clustering


ACM/IEEE-CS Joint Conference on Digital Libraries,


Statistical clustering is critical in designing scalable image retrieval systems. This paper presents 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, this overall similarity approach: (1) reduces the influence of inaccurate segmentation; (2) helps to clarify the semantics of a particular region; and (3) enables a simple querying interface for region-based image retrieval systems. The algorithm has been implemented as part of an 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. (Contains 41 references.) (Author)


Wang, J.Z. & Du, Y. (2001). Scalable Integrated Region-Based Image Retrieval Using IRM and Statistical Clustering. Presented at ACM/IEEE-CS Joint Conference on Digital Libraries 2001. Retrieved March 26, 2019 from .

This record was imported from ERIC on April 18, 2013. [Original Record]

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