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A Bayesian Method for Content-Based Image Retrieval by Use of Relevance Feedback

  • Ju-Lan Tao 
  • Yi-Ping Hung 
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2314)

Abstract

This paper proposes a new Bayesian method for content-based image retrieval using relevance feedback. In this method, the problem of contentbased image retrieval is first formulated as a two-class classification problem, where each image in the database can be classified as “relevant” or “nonrelevant” with respect to the query and the goal is to minimize the misclassification error. Then, the problem of image retrieval is further transferred into a simpler problem of ranking each image in the database by using a similarity measure that is basically a likelihood ratio. Here, the likelihood of the relevant class is modeled by a mixture of Gaussian distribution determined by the positive samples, and the likelihood of the non-relevant class is assumed to be an average of Gaussian kernels centered at negative samples. The experimental results have indicated that the proposed method has potential to become practical for content-based image retrieval.

Keywords

Image Retrieval Image Database Query Image Relevant Image Image Retrieval System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Ju-Lan Tao 
    • 1
    • 2
  • Yi-Ping Hung 
    • 1
    • 2
  1. 1.Academic SinicaInstitute of Information ScienceUSA
  2. 2.Department of Computer Science and Information EngineeringNational Taiwan UniversityUSA

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