A deep network model based on subspaces: A novel approach for image classification

Abstract

In this paper, we propose a novel deep neural network based on learning subspaces and convolutional neural network with applications in image classification. Recently, multistage PCA based filter banks have been successfully adopted in convolutional neural networks architectures in many applications including texture classification, face recognition and scene understanding. These approaches have shown to be powerful, with a straightforward implementation that enables a fast prototyping of efficient image classification systems. However, these architectures employ filters based on PCA, which may not achieve high discriminative features in more complicated computer vision datasets. In order to cope with the aforementioned drawback, we propose a Hybrid Subspace Neural Network (HS-Net). The proposed architecture employs filters from both PCA and discriminative filters banks from more sophisticated subspace methods, therefore achieving more representative and discriminative information. In addition, the use of hybrid architecture enables the use of supervised and unsupervised samples, depending on the application, making the introduced architecture quite attractive in practical terms. Exsperimental results on three publicly available datasets demonstrate the effectiveness and the practicability of the proposed architecture.