Multi-Attributed Dictionary Learning for Sparse Coding

被引:6
|
作者
Chiang, Chen-Kuo [1 ]
Su, Te-Feng [1 ]
Yen, Chih [1 ]
Lai, Shang-Hong [1 ]
机构
[1] Natl Tsing Hua Univ, Hsinchu 300, Taiwan
来源
2013 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV) | 2013年
关键词
RECOGNITION; REPRESENTATION;
D O I
10.1109/ICCV.2013.145
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We present a multi-attributed dictionary learning algorithm for sparse coding. Considering training samples with multiple attributes, a new distance matrix is proposed by jointly incorporating data and attribute similarities. Then, an objective function is presented to learn category-dependent dictionaries that are compact (closeness of dictionary atoms based on data distance and attribute similarity), reconstructive (low reconstruction error with correct dictionary) and label-consistent (encouraging the labels of dictionary atoms to be similar). We have demonstrated our algorithm on action classification and face recognition tasks on several publicly available datasets. Experimental results with improved performance over previous dictionary learning methods are shown to validate the effectiveness of the proposed algorithm.
引用
收藏
页码:1137 / 1144
页数:8
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