Image and Vision Computing
Conjugate gradient on Grassmann manifolds for robust subspace estimation
Image and Vision Computing
Advances in matrix manifolds for computer vision
Image and Vision Computing
On approximating the Riemannian 1-center
Computational Geometry: Theory and Applications
Averaging complex subspaces via a Karcher mean approach
Signal Processing
Semi-intrinsic mean shift on riemannian manifolds
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Polynomial regression on riemannian manifolds
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Dictionary-based face recognition from video
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Kernel analysis on Grassmann manifolds for action recognition
Pattern Recognition Letters
Multi-local model image set matching based on domain description
Pattern Recognition
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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 an 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 intraclass 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. Finally, we demonstrate the improved performance of these methods in a wide variety of vision applications such as activity recognition, video-based face recognition, object recognition from image sets, and activity-based video clustering.