  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.
