Prototype learning and collaborative representation using Grassmann manifolds for image set classification. (April 2020)

• Record Type: Journal Article
• Authors: Wei, Dong; Shen, Xiaobo; Sun, Quansen; Gao, Xizhan; Yan, Wenzhu
• Is Part Of: Pattern recognition. Volume 100(2020:Apr.)
• Publication Date: 2020-04
• Subjects: Image set classification -- Collaborative representation -- Prototype learning -- Grassmann manifolds; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2019.107123
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: Principle component and variation subspaces are constructed for an over-complete dictionary. A novel prototype and variation model (P+V) based collaborative representation for Grassmann manifolds is proposed to deal with image set classification naturally. Previous special matrices are generalized to common sparse matrices. Experimental results show the superiorities of our methods. Abstract: 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.