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Image Categorization Using Agglomerative Clustering Based Smoothed Dirichlet Mixtures

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Advances in Visual Computing (ISVC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12510))

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Abstract

With the rapid growth of multimedia data and the diversity of the available image contents, it becomes necessary to develop advanced machine learning algorithms for the purpose of categorizing and recognizing images. Hierarchical clustering methods have shown promising results in computer vision applications. In this paper, we present a new unsupervised image categorization technique in which we cluster images using an agglomerative hierarchical procedure and a dissimilarity metric is derived based on smoothed Dirichlet (SD) distribution. We propose a mixture of SD distributions and a maximum-likelihood learning framework, from which we derive a Kulback-Leibler divergence between two SD mixture models. Experiments on challenging images dataset that contains different indoor and outdoor places reveal the importance of the hierarchical clustering when categorizing images. The conducted tests prove the robustness of the proposed image categorization approach as compared to the other related-works.

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Author information

Authors and Affiliations

  1. Concordia Institute for Information and Systems Engineering (CIISE), Concordia University, Montreal, QC, Canada

    Fatma Najar & Nizar Bouguila

Authors
  1. Fatma Najar
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  2. Nizar Bouguila
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Corresponding authors

Correspondence to Fatma Najar or Nizar Bouguila .

Editor information

Editors and Affiliations

  1. University of Nevada Reno, Reno, NV, USA

    George Bebis

  2. Stony Brook University, Stony Brook, NY, USA

    Zhaozheng Yin

  3. Drexel University, Philadelphia, PA, USA

    Edward Kim

  4. RWTH Aachen University, Aachen, Germany

    Jan Bender

  5. University of Edinburgh, Edinburgh, UK

    Kartic Subr

  6. IBM Research – Cambridge, Cambridge, MA, USA

    Bum Chul Kwon

  7. University of Waterloo, Waterloo, ON, Canada

    Jian Zhao

  8. Graz University of Technology, Graz, Austria

    Denis Kalkofen

  9. The Hong Kong Polytechnic University, Hong Kong, Hong Kong

    George Baciu

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Najar, F., Bouguila, N. (2020). Image Categorization Using Agglomerative Clustering Based Smoothed Dirichlet Mixtures. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2020. Lecture Notes in Computer Science(), vol 12510. Springer, Cham. https://doi.org/10.1007/978-3-030-64559-5_3

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  • DOI: https://doi.org/10.1007/978-3-030-64559-5_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-64558-8

  • Online ISBN: 978-3-030-64559-5

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