182 research outputs found
Extrinsic Methods for Coding and Dictionary Learning on Grassmann Manifolds
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 over
the space of linear subspaces, which form Riemannian structures known as
Grassmann manifolds. 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 closed-form solutions 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 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.Comment: Appearing in International Journal of Computer Visio
Jumping Manifolds: Geometry Aware Dense Non-Rigid Structure from Motion
Given dense image feature correspondences of a non-rigidly moving object
across multiple frames, this paper proposes an algorithm to estimate its 3D
shape for each frame. To solve this problem accurately, the recent
state-of-the-art algorithm reduces this task to set of local linear subspace
reconstruction and clustering problem using Grassmann manifold representation
\cite{kumar2018scalable}. Unfortunately, their method missed on some of the
critical issues associated with the modeling of surface deformations, for e.g.,
the dependence of a local surface deformation on its neighbors. Furthermore,
their representation to group high dimensional data points inevitably introduce
the drawbacks of categorizing samples on the high-dimensional Grassmann
manifold \cite{huang2015projection, harandi2014manifold}. Hence, to deal with
such limitations with \cite{kumar2018scalable}, we propose an algorithm that
jointly exploits the benefit of high-dimensional Grassmann manifold to perform
reconstruction, and its equivalent lower-dimensional representation to infer
suitable clusters. To accomplish this, we project each Grassmannians onto a
lower-dimensional Grassmann manifold which preserves and respects the
deformation of the structure w.r.t its neighbors. These Grassmann points in the
lower-dimension then act as a representative for the selection of
high-dimensional Grassmann samples to perform each local reconstruction. In
practice, our algorithm provides a geometrically efficient way to solve dense
NRSfM by switching between manifolds based on its benefit and usage.
Experimental results show that the proposed algorithm is very effective in
handling noise with reconstruction accuracy as good as or better than the
competing methods.Comment: New version with corrected typo. 10 Pages, 7 Figures, 1 Table.
Accepted for publication in IEEE Conference on Computer Vision and Pattern
Recognition (CVPR) 2019. Acknowledgement added. Supplementary material is
available at https://suryanshkumar.github.io
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