Unsupervised Feature Learning for RGB-D Image Classification

Abstract

Motivated by the success of Deep Neural Networks in computer vision, we propose a deep Regularized Reconstruction Independent Component Analysis network (R\(^2\)ICA) for RGB-D image classification. In each layer of this network, we include a R\(^2\)ICA as the basic building block to determine the relationship between the gray-scale and depth images corresponding to the same object or scene. Implementing commonly used local contrast normalization and spatial pooling, we gradually enhance our network to be resilient to local variance resulting in a robust image representation for RGB-D image classification. Moreover, compared with conventional handcrafted feature-based RGB-D image representation, the proposed deep R\(^2\)ICA is a feedforward network. Hence, it is more efficient for image representation. Experimental results on three publicly available RGB-D datasets demonstrate that the proposed method consistently outperforms the state-of-the-art conventional, manually designed RGB-D image representation confirming its effectiveness for RGB-D image classification.