Discriminative canonical correlation analysis network for image classification

Abstract

Recently, convolutional neural network has attracted an increasing amount of attention in machine learning and computer vision areas, improving the performance of several related applications. Currently, many deep learning network architectures such as principal component analysis network (PCANet), linear discriminant analysis network (LDANet) and canonical correlation analysis network (CCANet) have been proposed for object and face classification. These architecture solutions have demonstrated high efficiency, with a simple implementation, providing a fast prototyping of efficient image classification applications. However, these solutions take advantage of filters that may not extract high discriminative features in more complicated computer vision problems (e.g. containing high degree of overlap between the distributions of the data). To generate more discriminative information, we introduce a discriminative canonical correlation network (DCCNet), that employs filters constructed from discriminative canonical correlations analysis (DCC). Learning filters from DCC ensures that the network will produce discriminative features, generating more representative and discriminative information. We demonstrate the applicability of DCCNet through experiments on four datasets.