Multi-view Discriminant Dictionary Learning via Learning View-specific and Shared Structured Dictionaries for Image Classification
Multi-view Discriminant Dictionary Learning via Learning View-specific and Shared Structured Dictionaries for Image Classification
复制标题
通过学习特定视图和共享结构化字典进行图像分类的多视图判别字典学习
DOI：
10.1007/s11063-016-9545-7
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发表时间：
2016-08
影响因子：
3.1
通讯作者：
Yue Dong
中科院分区：
文献类型：
--
作者：
Wu Fei;Jing Xiao-Yuan;Yue Dong
近年来，多视图字典学习技术引起了人们的广泛研究兴趣。尽管一些多视图字典学习方法已经得到解决，但仍有很大的改进空间。如何利用词典来挖掘和利用不同观点的多样性和有用的相关性信息还没有得到很好的研究。在本文中，我们提出了一种新颖的多视图字典学习方法，称为通过学习特定视图和共享结构化字典（MDVSD）的多视图判别字典学习，其目标是学习所有视图共享的结构化字典和多个特定于视图的结构化字典，每个视图特定于特定视图。共享字典与每个视图特定字典相结合来表示特定视图的数据。 MDVSD使得不同观点对应的特定观点词典不相关，以有效地探索不同观点的多样性。此外，我们将结构不相关性引入到共享字典学习过程中，从而可以有效地利用不同视图的有用相关信息。共享词典和特定视图词典中的词典原子与类标签具有对应关系，因此学习的词典具有良好的判别能力，并且获得的重建误差具有判别性。采用三个广泛使用的数据集作为测试数据。实验结果证明了该方法的有效性。
Recently, multi-view dictionary learning technique has attracted lots of research interest. Although some multi-view dictionary learning methods have been addressed, there exists much room for improvement. How to explore and utilize both the diversity and the useful correlation information of different views with dictionaries has not been well studied. In this paper, we propose a novel multi-view dictionary learning approach named multi-view discriminant dictionary learning via learning view-specific and shared structured dictionaries (MDVSD), which aims to learn a structured dictionary shared by all views and multiple view-specific structured dictionaries with each corresponding to a specific view. The shared dictionary is combined with each view-specific dictionary to represent data of the specific view. MDVSD makes the view-specific dictionaries corresponding to different views uncorrelated for effectively exploring the diversity of different views. Furthermore, we introduce structural uncorrelation into shared dictionary learning procedure, such that the useful correlation information of different views can be effectively exploited. Dictionary-atoms in shared and view-specific dictionaries have correspondence to class labels so that the learned dictionaries have favorable discriminant ability and the obtained reconstruction error is discriminative. Three widely used datasets are employed as test data. Experimental results demonstrate the effectiveness of the proposed approach.
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DOI：
10.1109/tsp.2009.2021636
发表时间：
2009-10-01
期刊：
IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society
影响因子：
--
作者：
Li YO;Adalı T;Wang W;Calhoun VD
通讯作者：
Calhoun VD
影响因子：
10.6
作者：
Gao, Shenghua;Tsang, Ivor Wai-Hung;Ma, Yi
通讯作者：
Ma, Yi
DOI：
10.1109/iccv.2013.394
发表时间：
2013-12
期刊：
2013 IEEE International Conference on Computer Vision
影响因子：
--
作者：
Jingjing Zheng;Zhuolin Jiang
通讯作者：
Jingjing Zheng;Zhuolin Jiang
DOI：
10.1609/aaai.v28i1.8986
发表时间：
2014-06
期刊：
--
影响因子：
--
作者：
Xiaoyuan Jing;R. Hu;Yang-Ping Zhu;Shanshan Wu;Chao Liang;Jing-yu Yang
通讯作者：
Xiaoyuan Jing;R. Hu;Yang-Ping Zhu;Shanshan Wu;Chao Liang;Jing-yu Yang
DOI：
10.1109/ism.2012.15
发表时间：
2012-12
期刊：
2012 IEEE International Symposium on Multimedia
影响因子：
--
作者：
Lei Gao;L. Qi;E. Chen;L. Guan
通讯作者：
Lei Gao;L. Qi;E. Chen;L. Guan