A Relevance Feedback Approach for Content Based Image Retrieval Using Gaussian Mixture Models

  • Apostolos Marakakis
  • Nikolaos Galatsanos
  • Aristidis Likas
  • Andreas Stafylopatis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4132)


In this paper a new relevance feedback (RF) methodology for content based image retrieval (CBIR) is presented. This methodology is based on Gaussian Mixture (GM) models for images. According to this methodology, the GM model of the query is updated in a probabilistic manner based on the GM models of the relevant images, whose relevance degree (positive or negative) is provided by the user. This methodology uses a recently proposed distance metric between probability density functions (pdfs) that can be computed in closed form for GM models. The proposed RF methodology takes advantage of the structure of this metric and proposes a method to update it very efficiently based on the GM models of the relevant and irrelevant images characterized by the user. We show with experiments the merits of the proposed methodology.


Gaussian Mixture Model Image Retrieval Relevance Feedback Content Base Image Retrieval Relevant Image 
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 2006

Authors and Affiliations

  • Apostolos Marakakis
    • 1
  • Nikolaos Galatsanos
    • 2
  • Aristidis Likas
    • 2
  • Andreas Stafylopatis
    • 1
  1. 1.School of Electrical and Computer EngineeringNational Technical University of AthensAthensGreece
  2. 2.Department of Computer ScienceUniversity of IoanninaIoanninaGreece

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