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Alternative TitleGeometry deep network image-set recognition method based on Grassmann manifolds
刘天赐1,2,3; 史泽林1,3; 刘云鹏1,3; 张英迪1,2,3
Source Publication红外与激光工程
Indexed ByEI ; CSCD
EI Accession number20183805818138
Contribution Rank1
Funding Organization中国科学院重点创新基金(Y6K4250401)
Keyword深度学习 Grassmann流形 黎曼优化 图像集识别


Other Abstract

In recent years, deep learning techniques have achieved great breakthrough for its powerful non -linear computations in the tasks of target recognition and detection. Existing deep networks were almost designed based on the precondition that the visual data reside on the Euclidean space. However, many data in computer vision have rigorous geometry of manifolds, i.e., image sets can be represented as Grassmann manifolds. The deep network was devised based on the non -Euclidean structure of the manifold-valued data, which combined the differential geometry and deep learning methods theoretically. Furthermore, a deep network for image-set recognition based on the Grassmann manifold was proposed. In the training process, the model was updated by the use of the backpropagation algorithm derived from the matrix chain rule. Learning of the weights can be transformed as the Riemannian optimization problem on the Grassmannian. The experimental results show that this method not only improves the accuracy of recognition, but also accelerates the training and test process in one magnitude.

Citation statistics
Cited Times:1[CSCD]   [CSCD Record]
Document Type期刊论文
Corresponding Author刘天赐
Recommended Citation
GB/T 7714
刘天赐,史泽林,刘云鹏,等. 基于Grassmann流形几何深度网络的图像集识别方法[J]. 红外与激光工程,2018,47(7):1-7.
APA 刘天赐,史泽林,刘云鹏,&张英迪.(2018).基于Grassmann流形几何深度网络的图像集识别方法.红外与激光工程,47(7),1-7.
MLA 刘天赐,et al."基于Grassmann流形几何深度网络的图像集识别方法".红外与激光工程 47.7(2018):1-7.
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