Multilinear Tensor Supervised Neighborhood Embedding Analysis for View-Based Object Recognition

  • Xian-Hua Han
  • Yen-Wei Chen
  • Xiang Ruan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6297)


In this paper, we propose a multilinear (N-Dimensional) Tensor Supervised Neighborhood Embedding (called ND-TSNE) for discriminant feature representation, which is used for view-based object recognition. ND-TSNE use a general N th order tensor discriminant and neighborhood-embedding analysis approach for object representation. The benefits of ND-TSNE include: (1) a natural way of representing data without losing structure information, i.e., the information about the relative positions of pixels or regions; (2) a reduction in the small sample size problem which occurs in conventional supervised learning because the number of training samples is much less than the dimensionality of the feature space; (3) a neighborhood structure preserving in tensor feature space for object recognition and a good convergence property in training procedure. With Tensor-subspace features, the random forests as a multi-way classifier is used for object recognition, which is much easier for training and testing compared with multi-way SVM. We demonstrate the performance advantages of our proposed approach over existing techniques using experiments on the COIL-100 and the ETH-80 datasets.


Random Forest Object Recognition Recognition Rate Alternative Less Square Average Recognition Rate 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Xian-Hua Han
    • 1
  • Yen-Wei Chen
    • 1
  • Xiang Ruan
    • 2
  1. 1.College of Information Science and EngineeringRitsumeikan UniversityKasatsu-shiJapan
  2. 2.Omron CorporationJapan

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