TR2011-084

Statistical Computations on Grassmann and Stiefel Manifolds for Image and Video-Based Recognition


    •  Turaga, P., Veeraraghavan, A., Srivastava, A., Chellappa, R., "Statistical Computations on Grassmann and Stiefel Manifolds for Image and Video-Based Recognition", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 33, No. 11, pp. 2273-2286, April 2011.
      BibTeX Download PDF
      • @article{Turaga2011apr,
      • author = {Turaga, P. and Veeraraghavan, A. and Srivastava, A. and Chellappa, R.},
      • title = {Statistical Computations on Grassmann and Stiefel Manifolds for Image and Video-Based Recognition},
      • journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
      • year = 2011,
      • volume = 33,
      • number = 11,
      • pages = {2273--2286},
      • month = apr,
      • url = {https://www.merl.com/publications/TR2011-084}
      • }
  • Research Area:

    Computer Vision


In this paper, we examine image and video based recognition applications where the underlying models have a special structure -- the linear subspace structure. We discuss how commonly used parametric models for videos and image-sets can be described using the unified framework of Grassmann and Stiefel manifolds. We first show that the parameters of linear dynamic models are finite dimensional linear subspaces of appropriate dimensions. Unordered image-sets as samples from a finite-dimensional linear subspace naturally fall under this framework. We show that the study of inference over subspaces can be naturally cast as an inference problem on the Grassmann manifold. To perform recognition using subspace-based models, we need tools from the Riemannian geometry of the Grassmann manifold. This involves a study of the geometric properties of the space, appropriate definitions of Riemannian metrics, and definition of geodesics. Further, we derive statistical modeling of inter- and intra-class variations that respect the geometry of the space. We apply techniques such as intrinsic and extrinsic statistics, to enable maximum-likelihood classification. We also provide algorithms for unsupervised clustering derived from the geometry of the manifold.