Multiple Scale Canonical Correlation Analysis Networks for Two-View Object Recognition

摘要

With the rapid development of representation learning, deep learning has been proved to be an effective technique to extract high level features. Many variants have been reported including convolu-tional neural network (CNN), principle component analysis networks (PCANet) and canonical correlation analysis networks (CCANet). The representative CCANet utilizes CCA to learn two-view multi-stage filter banks and achieves significant superiority to PCANet for object recognition. However, CCANet tends to only use the output feature of the last convolutional stage, which ignores the previous different scale features. To surmount this problem, in this paper, we present a novel method dubbed multiple scale canonical correlation analysis networks (MS-CCANet). Specifically, the MS-CCANet learns more discriminative information by stacking multi-scale features of all the convolutional stages together. Extensive experiments are conducted on ETH-80 dataset to verify the effectiveness of MS-CCANet. The results demonstrate that the proposed MS-CCANet outperforms the state-of-art methods including PCANet and CCANet.