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Learning visual categories through a sparse representation classifier based cross-category knowledge transfer
Ying Lu; Liming Chen; Alexandre Saidi; Zhaoxiang Zhang; Yunhong Wang
2014-10-27
会议名称International Conference on Image Processing
会议录名称ICIP 2014
会议日期27-30 October 2014
会议地点Paris, France
摘要To solve the challenging task of learning effective visual categories with limited training samples, we propose a new sparse representation classifier based transfer learning method, namely SparseTL, which propagates the cross-category knowledge from multiple source categories to the target category. Specifically, we enhance the target classification task in learning a both generative and discriminative sparse representation based classifier using pairs of source categories most positively and most negatively correlated to the target category. We further improve the discriminative ability of the classifier by choosing the most discriminative bins in the feature vector with a feature selection process. The experimental results show that the proposed method achieves competitive performance on the NUS-WIDE Scene database compared to several state of the art transfer learning algorithms while keeping a very efficient runtime.
关键词Visual Concept Recognition Transfer Learning Sparse Representation Computer Vision
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/13307
专题类脑智能研究中心
通讯作者Ying Lu
推荐引用方式
GB/T 7714
Ying Lu,Liming Chen,Alexandre Saidi,et al. Learning visual categories through a sparse representation classifier based cross-category knowledge transfer[C],2014.
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