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Ranking-Based Vocabulary Pruning in Bag-of-Features for Image Retrieval

  • Conference paper
Artificial Life and Computational Intelligence (ACALCI 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8955))

Abstract

Content-based image retrieval (CBIR) has been applied to a variety of medical applications, e.g., pathology research and clinical decision support, and bag-of-features (BOF) model is one of the most widely used techniques. In this study, we address the problem of vocabulary pruning to reduce the influence from the redundant and noisy visual words. The conditional probability of each word upon the hidden topics extracted using probabilistic Latent Semantic Analysis (pLSA) is firstly calculated. A ranking method is then proposed to compute the significance of the words based on the relationship between the words and topics. Experiments on the publicly available Early Lung Cancer Action Program (ELCAP) database show that the method can reduce the number of words required while improving the retrieval performance. The proposed method is applicable to general image retrieval since it is independent of the problem domain.

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Author information

Authors and Affiliations

  1. Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, University of Sydney, Australia

    Fan Zhang, Yang Song, Weidong Cai, Sidong Liu, Siqi Liu & David Dagan Feng

  2. School of Computer Science, Carnegie Mellon University, United States

    Alexander G. Hauptmann

  3. Med-X Research Institute, Shanghai Jiaotong University, China

    David Dagan Feng

  4. Intel Science and Technology Center on Embedded Computing, Carnegie Mellon University, United States

    Mei Chen

Authors
  1. Fan Zhang
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  2. Yang Song
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  3. Weidong Cai
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  4. Alexander G. Hauptmann
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  5. Sidong Liu
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  6. Siqi Liu
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  7. David Dagan Feng
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  8. Mei Chen
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Editor information

Editors and Affiliations

  1. School of Electrical Engineering and Computer Science, The University of Newcastle, 2308, Callaghan, NSW, Australia

    Stephan K. Chalup

  2. School of Computer Science and Engineering, The University of New South Wales, 2052, Sydney, NSW, Australia

    Alan D. Blair

  3. Faculty of Business, Bond University, 4226, Robina, QLD, Australia

    Marcus Randall

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Zhang, F. et al. (2015). Ranking-Based Vocabulary Pruning in Bag-of-Features for Image Retrieval. In: Chalup, S.K., Blair, A.D., Randall, M. (eds) Artificial Life and Computational Intelligence. ACALCI 2015. Lecture Notes in Computer Science(), vol 8955. Springer, Cham. https://doi.org/10.1007/978-3-319-14803-8_34

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  • DOI: https://doi.org/10.1007/978-3-319-14803-8_34

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14802-1

  • Online ISBN: 978-3-319-14803-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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