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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
Conference NameInternational Conference on Image Processing
Source PublicationICIP 2014
Conference Date27-30 October 2014
Conference PlaceParis, France
AbstractTo 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.
KeywordVisual Concept Recognition Transfer Learning Sparse Representation Computer Vision
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/13307
Collection类脑智能研究中心
Corresponding AuthorYing Lu
Recommended Citation
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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