Extrinsic Methods for Coding and Dictionary Learning on Grassmann Manifolds

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• 作者：Mehrtash Harandi ; Richard Hartley ; Chunhua Shen…
• 关键词：Riemannian geometry ; Grassmann manifolds ; Sparse coding ; Dictionary learning
• 刊名：International Journal of Computer Vision
• 出版年：2015
• 出版时间：September 2015
• 年：2015
• 卷：114
• 期：2-3
• 页码：113-136
• 全文大小：2,098 KB
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• 作者单位：Mehrtash Harandi (1) (2)
  Richard Hartley (1) (2)
  Chunhua Shen (3)
  Brian Lovell (4)
  Conrad Sanderson (2) (4)
  
  1. College of Engineering and Computer Science, Australian National University, Canberra, Australia
  2. NICTA, Canberra, Australia
  3. School of Computer Science, The University of Adelaide, Adelaide, SA, 5005, Australia
  4. The University of Queensland, Brisbane, Australia
  
• 刊物类别：Computer Science
• 刊物主题：Computer Imaging, Vision, Pattern Recognition and Graphics
  Artificial Intelligence and Robotics
  Image Processing and Computer Vision
  Pattern Recognition
  
• 出版者：Springer Netherlands
• ISSN：1573-1405

文摘

Sparsity-based representations have recently led to notable results in various visual recognition tasks. In a separate line of research, Riemannian manifolds have been shown useful for dealing with features and models that do not lie in Euclidean spaces. With the aim of building a bridge between the two realms, we address the problem of sparse coding and dictionary learning in Grassmann manifolds, i.e., the space of linear subspaces. To this end, we propose to embed Grassmann manifolds into the space of symmetric matrices by an isometric mapping. This in turn enables us to extend two sparse coding schemes to Grassmann manifolds. Furthermore, we propose an algorithm for learning a Grassmann dictionary, atom by atom. Lastly, to handle non-linearity in data, we extend the proposed Grassmann sparse coding and dictionary learning algorithms through embedding into higher dimensional Hilbert spaces. Experiments on several classification tasks (gender recognition, gesture classification, scene analysis, face recognition, action recognition and dynamic texture classification) show that the proposed approaches achieve considerable improvements in discrimination accuracy, in comparison to state-of-the-art methods such as kernelized Affine Hull Method and graph-embedding Grassmann discriminant analysis. Keywords Riemannian geometry Grassmann manifolds Sparse coding Dictionary learning