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Tactile Object Recognition with Semi-Supervised Learning

  • Shan Luo
  • Xiaozhou Liu
  • Kaspar Althoefer
  • Hongbin LiuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9245)

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.

Keywords

Tactile sensors Object recognition Robot tactile systems 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Shan Luo
    • 1
  • Xiaozhou Liu
    • 1
  • Kaspar Althoefer
    • 1
  • Hongbin Liu
    • 1
    Email author
  1. 1.Department of InformaticsKing’s College LondonLondonUK

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