CASIA OpenIR  > 模式识别国家重点实验室  > 多媒体计算与图形学
Graph-Guided Fusion Penalty Based Sparse Coding for Image Classification
Yang, Xiaoshan; Zhang, Tianzhu; Xu, Changsheng; Xu CS(徐常胜)
Conference NamePacific-Rim Conference on Multimedia (PCM)
Source PublicationPCM
Conference Date2013
Conference Place南京
In image classification, conventional sparse coding only encodes
local features independently. As a result, the similar local features
may be encoded into code vectors with large discrepancy. This
sensitiveness has became the bottleneck of the traditional sparse coding
based image classification methods. In this paper, we propose a novel
graph-guided fusion penalty based sparse coding method. To alleviate
the sensitiveness of the traditional sparse coding, our approach constrains
that the similar local features are encoded into similar code vectors. To
achieve this goal, we add the popular graph-guided fusion penalty term
into the traditional l1-regularized sparse coding formulation. Finally, we
adopt the multi-task form of the smoothing proximal gradient method
to solve our optimization problem efficiently. Experimental results on 3
benchmark datasets demonstrate the effectiveness of our improved sparse
coding method in image classification.
KeywordImage Classification Sparse Coding Smoothing Proximal Gradient
Document Type会议论文
Corresponding AuthorXu CS(徐常胜)
Recommended Citation
GB/T 7714
Yang, Xiaoshan,Zhang, Tianzhu,Xu, Changsheng,et al. Graph-Guided Fusion Penalty Based Sparse Coding for Image Classification[C],2013.
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