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Prototype learning and collaborative representation using Grassmann manifolds for image set classification
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DOI：10.1016/j.patcog.2019.107123.png)
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Image set classification using manifolds is becoming increasingly more attractive since it considers non-Euclidean geometry. However, with the success of dictionary learning for image set classification using manifolds, how to learn an over-complete dictionary is still challenging. This paper proposes a novel prototype subspace learning method, in which a set of images is represented by a linear subspace and then mapped onto a Grassmann manifold. With this subspace representation, class prototypes and intra-class differences can be represented as principal components and variation subspaces, respectively. Isometric mapping further maps the manifolds into the symmetrical space via collaborative representation, which permits a closed-term solution. The proposed method is evaluated for face recognition, object recognition and action recognition. Extensive experimental results on the Honda, Extended YaleB, ETH-80 and Cambridge-Gesture datasets verify the superiority of the proposed method over the state-of-the-art methods. (C) 2019 Elsevier Ltd. All rights reserved.
Keyword:
Image set classification
Collaborative representation
Prototype learning
Grassmann manifolds
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IF:
7.6
论文数:
1.3W
被引数:
4.5W
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