Graph-Guided Fusion Penalty Based Sparse Coding for Image Classification
Yang, Xiaoshan; Zhang, Tianzhu; Xu, Changsheng; Xu CS(徐常胜)
2013
会议名称Pacific-Rim Conference on Multimedia (PCM)
会议录名称PCM
会议日期2013
会议地点南京
摘要
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.
关键词Image Classification Sparse Coding Smoothing Proximal Gradient
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/11762
专题模式识别国家重点实验室_多媒体计算与图形学
通讯作者Xu CS(徐常胜)
作者单位中科院自动化研究所
推荐引用方式
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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