TY - JOUR T1 - Learning Block Group Spase Representation Combined with Convolutional Neural Networks for RGB-D Object Recognition AU - Shuqin Tu, Yueju Xue, Jinfeng Wang, Xiaolin Huang & Xiao Zhang JO - Journal of Fiber Bioengineering and Informatics VL - 4 SP - 603 EP - 613 PY - 2014 DA - 2014/07 SN - 7 DO - http://doi.org/10.3993/jfbi12201413 UR - https://global-sci.org/intro/article_detail/jfbi/4814.html KW - RGB-D KW - Convolutional Neural Networks KW - Block Group Sparse Coding KW - Classification Recognition KW - Feature Learning Methods AB - RGB-D (Red, Green and Blue-Depth) cameras are novel sensing systems that can improve image recognition by providing high quality color and depth information in computer vision. In this paper we propose a model to study feature representation of combined Convolutional Neural Networks (CNN) and Block 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 feature representation for classification by incorporating both the group structure for low-level features and the block structure for the dictionary in subsequent learning processes. Experimental results show that the CNN-BGSC approach has higher accuracy on a household RGB-D object dataset by linear predictive classifier than using Convolutional and Recursive Neural Networks (CNN-RNN), Group Sparse Coding (GSC), and Sparse Representation base Classification (SRC).

Shuqin Tu, Yueju Xue, Jinfeng Wang, Xiaolin Huang and Xiao Zhang. (2014). Learning Block Group Spase Representation Combined with Convolutional Neural Networks for RGB-D Object Recognition. Journal of Fiber Bioengineering and Informatics. 7 (4). 603-613. doi:10.3993/jfbi12201413