View-Based Object Recognition Using ND Tensor Supervised Neighborhood Embedding

Xian-Hua HAN  Yen-Wei CHEN  Xiang RUAN  

IEICE TRANSACTIONS on Information and Systems   Vol.E95-D   No.3   pp.835-843
Publication Date: 2012/03/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E95.D.835
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Pattern Recognition
tensor analysis,  supervised neighborhood embedding,  subspace learning,  random forests,  view-based object recognition,  

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In this paper, we propose N-Dimensional (ND) Tensor Supervised Neighborhood Embedding (ND TSNE) for discriminant feature representation, which is used for view-based object recognition. ND TSNE uses a general Nth 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) preserving a neighborhood structure in tensor feature space for object recognition and a good convergence property in training procedure. With Tensor-subspace features, the random forests is used as a multi-way classifier 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.