Tensor object classification via multilinear discriminant analysis network

Rui Zeng; Jiasong Wu; Lotfi Senhadji; Huazhong Shu

ICASSP 2015accepted0 citations

Tensor object classification via multilinear discriminant analysis network

Rui Zeng, Jiasong Wu, Lotfi Senhadji, Huazhong Shu

Abstract

This paper proposes an multilinear discriminant analysis network (MLDANet) for the recognition of multidimensional objects, knows as tensor objects. The MLDANet is a variation of linear discriminant analysis network (LDANet) and principal component analysis network (PCANet), both of which are the recently proposed deep learning algorithms. The MLDANet consists of three parts: 1) The encoder learned by MLDA from tensor data. 2) Features maps obtained from decoder. 3) The use of binary hashing and histogram for feature pooling. A learning algorithm for MLDANet is described. Evaluations on UCF11 database indicate that the proposed MLDANet outperforms the PCANet, LDANet, MPCA+LDA, and MLDA in terms of classification for tensor objects.

BibTeX

@inproceedings{icassp2015_tensorobjectclas,
  title = {Tensor object classification via multilinear discriminant analysis network},
  author = {Rui Zeng and Jiasong Wu and Lotfi Senhadji and Huazhong Shu},
  booktitle = {ICASSP 2015},
  year = {2015}
}