Learning Block Group Spase Representation Combined with Convolutional Neural Networks for RGB-D Object Recognition (Journal of Fiber Bioengineering and Informatics, 2014)
RGB-D (Red, Green and Blue-Depth) cameras are novel sensing systems that can improve imagerecognition by providing high quality color and depth information in computer vision. In this paper wepropose a model to study feature representation of combined Convolutional Neural Networks (CNN) andBlock Group Sparse Coding (BGSC). Firstly, CNN is used to extract low-level features from raw RGB-D images directly by applying unsupervised algorithm. Then, BGSC is used to obtain higher featurerepresentation for classification by incorporating both the group structure for low-level features and theblock structure for the dictionary in subsequent learning processes. Experimental results show that theCNN-BGSC approach has higher accuracy on a household RGB-D object dataset by linear predictiveclassifier than using Convolutional and Recursive Neural Networks (CNN-RNN), Group Sparse Coding(GSC), and Sparse Representation base Classification (SRC).
Keywords: RGB-D; Convolutional Neural Networks; Block Group Sparse Coding; Classification Recognition; Feature Learning Methods