Scalable integrated region-based image retrieval using IRM and statistical clustering

  • Authors:

  • James Z. Wang;Yanping Du

  • Affiliations:

  • School of Information Sciences and Technology, The Pennsylvania State University, University Park, PA and Department of Computer Science and Engineering, and e-Business Research Center, Department ...;Cisco Systems, Inc., Department of Electrical Engineering and School of Information Sciences and Technology, The Pennsylvania State University, University Park, PA

  • Venue:
  • Proceedings of the 1st ACM/IEEE-CS joint conference on Digital libraries
  • Year:
  • 2001

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Abstract

Statistical clustering is critical in designing scalable image retriev al 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.