Support vector machines for region-based image retrieval

  • Authors:
  • Feng Jing;Mingjing Li;Hong-Jiang Zhang;Bo Zhang

  • Affiliations:
  • State Key Lab of Intelligent Technol. & Syst., Beijing, China;Media Lab., MIT, Cambridge, MA, USA;Dept. of Electr. Eng., Princeton Univ., NJ, USA;-

  • Venue:
  • ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
  • Year:
  • 2003

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In this paper, the application of support vector machines (SVM) in relevance feedback for region-based image retrieval is investigated. Both the one class SVM as a class distribution estimator and two classes SVM as a classifier are taken into account. For the latter, two representative display strategies are studied. Since the common kernels often rely on inner product or L/sub p/ norm in the input space, they are infeasible in the region-based image retrieval systems that use variable-length representations. To resolve the issue, a new kind of kernel that is a generalization of Gaussian kernel is proposed. Experimental results on a database of 10,000 general-purpose images demonstrate the effectiveness and robustness of the proposed approach.