Efficient region-based image retrieval

  • Authors:
  • Roger Weber;Michael Mlivoncic

  • Affiliations:
  • Swiss Federal Institute of Technology (ETH), Zurich, Switzerland;Swiss Federal Institute of Technology (ETH), Zurich, Switzerland

  • Venue:
  • CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
  • Year:
  • 2003

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Abstract

Region-based image retrieval(RBIR) was recently proposed as an extension of content-based image retrieval(CBIR). An RBIR system automatically segments images into a variable number of regions, and extracts for each region a set of features. Then, a dissimilarity function determines the distance between a database image and a set of reference regions. Unfortunately, the large evaluation costs of the dissimilarity function are restricting RBIR to relatively small databases. In this paper, we apply a multi-step approach to enable region-based techniques for large image collections. We provide cheap lower and upper bounding distance functions for a recently proposed dissimilarity measure. As our experiments show, these bounding functions are so tight, that we have to evaluate the expensive distance function for less than 0.5\%of the images. For a typical image database with more than 370,000images, our multi-step approach improved retrieval performance by a factor of more than5 compared to the currently fastest methods.