The recently proposed principal component analysis network (PCANet) has been proved high performance for visual content classification. In this letter, we develop a tensorial extension of PCANet, namely, multilinear principal analysis component network (MPCANet), for tensor object classification. Compared to PCANet, the proposed MPCANet uses the spatial structure and the relationship between each dimension of tensor objects much more efficiently. Experiments were conducted on different visual content datasets including UCF sports action video sequences database and UCF11 database. The experimental results have revealed that the proposed MPCANet achieves higher classification accuracy than PCANet for tensor object classification.
@article{arxiv.1411.1171,
title = {Multilinear Principal Component Analysis Network for Tensor Object Classification},
author = {Rui Zeng and Jiasong Wu and Zhuhong Shao and Lotfi Senhadji and Huazhong Shu},
journal= {arXiv preprint arXiv:1411.1171},
year = {2014}
}