Image Retrieval Using Mixture Models and EM Algorithm

  • Micheline Najjar
  • Christophe Ambroise
  • Jean Pierre Cocquerez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2749)


This paper presents an original system for interactive content-based image retrieval (CBIR). A novel approach for searching by similarity is introduced. It is based on a classification of the index database using mixture models and the EM algorithm. The presented retrieval system is evaluated and validated using a medical image database and the Washington University heterogeneous database (ANN).


Mixture Model Image Retrieval Relevance Feedback 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.


  1. 1.
    C. Ambroise, G. Govaert, “Em algorithm for partially known labels”. IFCS2000, 7th Conference of the International Federation of Classication Societies, Namur, Belgique, pp.11–14, 2000.Google Scholar
  2. 2.
    Boutry, N., Lard, A., Solau, E., Flipo, R., Cotten, A.: The Usefulness of MR Imaging in Early Rheumatoid Arthritis. RSNA, Chicago (2001) 25–30Google Scholar
  3. 3.
    A. Dempster, N. Laird, D. Rubin, “Maximum likehood from incomplete data via the EM algorithm”, Journal of the Royal Statistical Society, Vol 39, pp.1–38, 1977.zbMATHMathSciNetGoogle Scholar
  4. 4.
    J. Fournier, M. Cord, S.F. Philipp, “Retin: A content-based image indexing and retrieval system”, Pattern Analysis and Applications Journal, Special issue on image indexation, Vol. 4, n 2/3, pp.153–173, 2001.zbMATHGoogle Scholar
  5. 5.
    T. Hastie, R. Tibshirani, “Discriminant Analysis by Gaussian Mixtures”, Journal of the Royal Statistical Society B, Vol. 58, pp. 155–176, 1996.zbMATHMathSciNetGoogle Scholar
  6. 6.
    K. Nigam, A. McCallum, S. Thrun, T. Mitchell, “Text Classification from Labeled and Unlabeled Documents using EM”, Machine learning, Vol.39, n2/3, pp. 135–167, 2000.CrossRefGoogle Scholar
  7. 7.
    S. Santini, R. Jain, “Similarity Measures”, IEEE T-PAMI, Vol. 21, n 9, 1999.Google Scholar
  8. 8.
    C. Shyu, C. Brodley, A. Kak, A. Kosaka, “Assert: A Phisician-in-the-Loop Content-Based Retrieval System for HRCT Image Databases”, Computer Vision and Image Understanding, Vol. 75, n 1/2, pp. 111–132, 1999.CrossRefGoogle Scholar
  9. 9.
    A. Smeulders, M. Worring, S. Santani, A. Gupta, R. Jain, “Content-Based Image Retrieval at the end of the Early Years”, IEEE transactions on pattern analysis and machine intelligence, Vol. 22, n12, 2000.Google Scholar
  10. 10.
    Y. Wu, Q. Tian, T. Huang, “Discriminant-EM Algorithm with Application to Image Retrieval”, Proc. Computer Vision and Pattern Recognition, pp. 222–227, 2000.Google Scholar
  11. 11.
    S. Osher, J. Sethian,: “Fronts Propagating with Curvature-Dependent Speed: Algorithms based on Hamilton-Jacobi Formulations”. J. of Computational physics, Vol. 79, pp. 12–49, 1988.zbMATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Micheline Najjar
    • 1
  • Christophe Ambroise
    • 1
  • Jean Pierre Cocquerez
    • 1
  1. 1.Heudiasyc UMR CNRS 6599Universite de technologie de compiegneCompiegneFrance

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