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Tactile Object Recognition with Semi-Supervised Learning

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Intelligent Robotics and Applications (ICIRA 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9245))

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Abstract

This paper introduced a novel approach to recognize objects with tactile images by utilizing semi-supervised learning approaches. In tactile object recognition, the data are normally insufficient to build robust training models. Thus the model of Ensemble Manifold Regularization, which combines concepts of multi-view learning and semi-supervised learning, is adapted in tactile sensing to achieve better recognition accuracy. Different outputs of classic bag of words with different dictionary sizes are considered as different views to produce an optimized one based on multiple graphs learning optimization. In the experiments 12 objects were used to compare the classification performances of our proposed approach and the classic BoW model and it is proved that our proposed method outperforms the classic BoW framework and objects with similar features can be better classified.

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Author information

Authors and Affiliations

  1. Department of Informatics, King’s College London, London, UK

    Shan Luo, Xiaozhou Liu, Kaspar Althoefer & Hongbin Liu

Authors
  1. Shan Luo
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  2. Xiaozhou Liu
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  3. Kaspar Althoefer
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  4. Hongbin Liu
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Corresponding author

Correspondence to Hongbin Liu .

Editor information

Editors and Affiliations

  1. University of Portsmouth, Portsmouth, United Kingdom

    Honghai Liu

  2. Tokyo Metropolitan University, Tokyo, Japan

    Naoyuki Kubota

  3. Shanghai Jiao Tong University, Shanghai, China

    Xiangyang Zhu

  4. Karlsruhe Institute of Technology, Karlsruhe, Germany

    Rüdiger Dillmann

  5. University of Portsmouth, Portsmouth, United Kingdom

    Dalin Zhou

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© 2015 Springer International Publishing Switzerland

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Luo, S., Liu, X., Althoefer, K., Liu, H. (2015). Tactile Object Recognition with Semi-Supervised Learning. In: Liu, H., Kubota, N., Zhu, X., Dillmann, R., Zhou, D. (eds) Intelligent Robotics and Applications. ICIRA 2015. Lecture Notes in Computer Science(), vol 9245. Springer, Cham. https://doi.org/10.1007/978-3-319-22876-1_2

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  • DOI: https://doi.org/10.1007/978-3-319-22876-1_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22875-4

  • Online ISBN: 978-3-319-22876-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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