Learning Block Group Spase Representation Combined with Convolutional Neural Networks for RGB-D Object Recognition
DOI:
10.3993/jfbi12201413
Journal of Fiber Bioengineering & Informatics, 7 (2014), pp. 603-613.
Published online: 2014-07
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@Article{JFBI-7-603,
author = {},
title = {Learning Block Group Spase Representation Combined with Convolutional Neural Networks for RGB-D Object Recognition},
journal = {Journal of Fiber Bioengineering and Informatics},
year = {2014},
volume = {7},
number = {4},
pages = {603--613},
abstract = {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).},
issn = {2617-8699},
doi = {https://doi.org/10.3993/jfbi12201413},
url = {http://global-sci.org/intro/article_detail/jfbi/4814.html}
}
TY - JOUR
T1 - Learning Block Group Spase Representation Combined with Convolutional Neural Networks for RGB-D Object Recognition
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 & Xiao Zhang. (2019). 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
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