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A Novel Hierarchical Statistical Model for Count Data Modeling and Its Application in Image Classification

  • Ali Shojaee Bakhtiari
  • Nizar Bouguila
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7664)

Abstract

The problem that we elaborate in this work is developing and comparing statistical models for learning hierarchical image categories from a structural point of view. Previously different statistical models have been proposed based on different statistical schemes for dealing with hierarchical structures. In this work following the lead of the previous models we develop our own hierarchical model and we make a thorough comparison between the existing and the proposed models. Our main contribution in this work is the utilization of Beta-Liouville distribution as a replacement for Dirichlet distribution, which is traditionally used for prior distribution modeling, and deriving the criteria for making it compatible to hierarchical data modeling. For the development of our statistical model, we make extensive use of the Bag of the visual words model and the concept of count data in machine learning.

Keywords

Statistical modeling Object classification Bayesian inference Dirichlet distribution Beta-Liouville distribution 

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References

  1. 1.
    Peelen, M.V., Fei-Fei, L., Kastner, S.: Neural Mechanisms of Rapid Natural Scene Categorization in Human Visual Cortex. Nature, 94–97 (2009)Google Scholar
  2. 2.
    Fei-Fei, L., VanRullen, R., Koch, C., Perona, P.: Rapid Natural Scene Categorization in the Near Absence of Attention. National Academy of Sciences, 9596–9601 (2002)Google Scholar
  3. 3.
    Brunelli, R., Mich, O.: Image Retrieval by Examples. IEEE Transactions on Multimedia, 164–171 (2000)Google Scholar
  4. 4.
    Bouguila, N., Ziou, D.: Improving Content Based Image Retrieval Systems Using Finite Multinomial Dirichlet Mixture. In: 14th IEEE Workshop on Machine Learning for Signal Processing, pp. 23–32 (2004)Google Scholar
  5. 5.
    Long, W., Srihann, S.: Land Cover Classification of SSC Image: Unsupervised and Supervised Classification Using ERDAS Imagine. In: International Geoscience and Remote Sensing Symposium, pp. 2707–2712 (2004)Google Scholar
  6. 6.
    Greenspan, H., Pinhas, A.T.: Medical Image Categorization and Retrieval for PACS Using the GMM-KL Framework. IEEE Transactions on Information Technology in Biomedicine 11, 190–202 (2007)CrossRefGoogle Scholar
  7. 7.
    Sivic, J., Russell, B.C., Zisserman, A., Freeman, W.T., Efros, A.A.: Unsupervised Discovery of Visual Object Class Hierarchies. In: Computer Vision and Pattern Recognition, pp. 1–8 (2008)Google Scholar
  8. 8.
    Bakhtiari, A.S., Bouguila, N.: An Expandable Hierarchical Statistical Framework for Count Data Modeling and its Application to Object Classification. In: Tools with Artificial Intelligence (ICTAI), pp. 817–824Google Scholar
  9. 9.
    Fei-Fei, L., Perona, P.: A Bayesian Hierarchical Model for Learning Natural Scene Categories. In: Proc. of CVPR, pp. 524–531 (2005)Google Scholar
  10. 10.
    Csurka, G., Dance, C.R., Fan, L., Willamowski, J., Bray, C.: Visual Categorization with Bags of Keypoints. In: Workshop on Statistical Learning in Computer Vision, ECCV, pp. 1–22 (2004)Google Scholar
  11. 11.
    Chapelle, O., Haffner, P., Vapnik, V.N.: Support Vector Machines for Histogram-based Image Classification. IEEE Transactions on Neural Networks 10, 1055–1064 (1999)CrossRefGoogle Scholar
  12. 12.
    Blei, D., Griffiths, T., Jordan, M., Tenenbaum, J., Blei, D., Griffiths, T., Jordan, M., Tenenbaum, J.: Hierarchical Topic Models and the Nested Chinese Restaurant Process. MIT Press (2004)Google Scholar
  13. 13.
    Veeramachaneni, S., Sona, D., Avesani, P.: Hierarchical Dirichlet Model for Document Classification. In: International Conference on Machine Learning, pp. 928–935 (2005)Google Scholar
  14. 14.
    Bakhtiari, A.S., Bouguila, N.: A Hierarchical Statistical Model for Object Classification. In: 2010 IEEE International Workshop on Multimedia Signal Processing (MMSP), pp. 493–498 (2010)Google Scholar
  15. 15.
    Gupta, A.K., Song, D.: Generalized Liouville Distribution. Computers & Mathematics with Applications 32, 103–109 (1996)MathSciNetzbMATHCrossRefGoogle Scholar
  16. 16.
    Bouguila, N.: A Liouville-Based Approach for Discrete Data Categorization. In: Kuznetsov, S.O., Ślęzak, D., Hepting, D.H., Mirkin, B.G. (eds.) RSFDGrC 2011. LNCS, vol. 6743, pp. 330–337. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  17. 17.
    Lee, J.J.: LIBPMK: A Pyramid Match Toolkit. Technical report. MIT Computer Science and Artificial Intelligence Laboratory (2008)Google Scholar
  18. 18.
    Lowe, D.G.: Object Recognition from Local Scale-Invariant Features. In: International Conference on Computer Vision, pp. 1150–1157 (1999)Google Scholar
  19. 19.
    Bouguila, N., Ziou, D.: MML-Based Approach for High-Dimensional Unsupervised Learning Using the Generalized Dirichlet Mixture. In: Proceedings of CVPR, p. 53 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ali Shojaee Bakhtiari
    • 1
  • Nizar Bouguila
    • 1
  1. 1.Department of Electrical and Computer Engineering and Concordia Institute for Information Systems EngineeringConcordia UniversityMontrealCanada

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