Constraint Based Region Matching for Image Retrieval

  • Authors:

  • Tao Wang;Yong Rui;Jia-Guang Sun

  • Affiliations:

  • Department of Computer Science and Technology, Tsinghua University, Beijing 100084, P.R. China. wangtao@cg.cs.tsinghua.edu.cn;Microsoft Research, One Microsoft Way, Redmond, WA 98052, USA. yongrui@microsoft.com;Department of Computer Science and Technology, Tsinghua University, Beijing 100084, P.R. China. sunjg@mail.tsinghua.edu.cn

  • Venue:
  • International Journal of Computer Vision - Special Issue on Content-Based Image Retrieval
  • Year:
  • 2004

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Abstract

Objects and their spatial relationships are important features for human visual perception. In most existing content-based image retrieval systems, however, only global features extracted from the whole image are used. While they are easy to implement, they have limited power to model semantic-level objects and spatial relationship. To overcome this difficulty, this paper proposes a constraint-based region matching approach to image retrieval. Unlike existing region-based approaches where either individual regions are used or only first-order constraints are modeled, the proposed approach formulates the problem in a probabilistic framework and simultaneously models both first-order region properties and second-order spatial relationships for all the regions in the image. Specifically, in this paper we present a complete system that includes image segmentation, local feature extraction, first- and second-order constraints, and probabilistic region weight estimation. Extensive experiments have been carried out on a large heterogeneous image collection with 17,000 images. The proposed approach achieves significantly better performance than the state-of-the-art approaches.