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3D Object Classification Using Deep Belief Networks

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MultiMedia Modeling (MMM 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8326))

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Abstract

Extracting features with strong expressive and discriminative ability is one of key factors for the effectiveness of 3D model classifier. Lots of research work has illustrated that deep belief networks (DBN) have enough power to represent the distributions of input data. In this paper, we apply DBN for extracting the features of 3D model. After implementing a contrastive divergence method, we obtain a trained-well DBN, which can powerfully represent the input data. Therefore, the feature from the output of last layer is acquired. This procedure is unsupervised. Due to the limit of labeled data, a semi-supervised method is utilized to recognize 3D objects using the feature obtained from the trained DBN. The experiments are conducted in the publicly available Princeton Shape Benchmark (PSB), and the experimental results demonstrate the effectiveness of our method.

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Author information

Authors and Affiliations

  1. School of Computer Science & Engineering, Beihang University, Beijing, 100191, P.R. China

    Biao Leng, Xiangyang Zhang, Ming Yao & Zhang Xiong

Authors
  1. Biao Leng
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  2. Xiangyang Zhang
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  3. Ming Yao
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  4. Zhang Xiong
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Editor information

Editors and Affiliations

  1. School of Computing, Dublin City University, 9, Dublin, Ireland

    Cathal Gurrin

  2. Fakultät IV für Elektrotechnik und Informatik, Technische Universität Berlin / DAI-Labor, 10587, Berlin, Germany

    Frank Hopfgartner

  3. Department of Information and Computing Sciences, Universiteit Utrecht, 3584, Utrecht, CC, The Netherlands

    Wolfgang Hurst

  4. UiT The Arctic University of Norway, 9019, Tromsø, Norway

    HÃ¥vard Johansen

  5. Singapore University of Technology and Design, Singapore

    Hyowon Lee

  6. School of Electrical Engineering, Dublin City University, Ireland

    Noel O’Connor

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© 2014 Springer International Publishing Switzerland

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Leng, B., Zhang, X., Yao, M., Xiong, Z. (2014). 3D Object Classification Using Deep Belief Networks. In: Gurrin, C., Hopfgartner, F., Hurst, W., Johansen, H., Lee, H., O’Connor, N. (eds) MultiMedia Modeling. MMM 2014. Lecture Notes in Computer Science, vol 8326. Springer, Cham. https://doi.org/10.1007/978-3-319-04117-9_12

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  • DOI: https://doi.org/10.1007/978-3-319-04117-9_12

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-04116-2

  • Online ISBN: 978-3-319-04117-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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