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Found 6 papers, 2 papers with code
More About Covariance Descriptors for Image Set Coding: Log-Euclidean Framework based Kernel Matrix Representation
We consider a family of structural descriptors for visual data, namely covariance descriptors (CovDs) that lie on a non-linear symmetric positive definite (SPD) manifold, a special type of Riemannian manifolds.
Grassmannian Discriminant Maps (GDM) for Manifold Dimensionality Reduction with Application to Image Set Classification
The core of the method is a new discriminant function for metric learning and dimensionality reduction.
Riemannian kernel based Nyström method for approximate infinite-dimensional covariance descriptors with application to image set classification
We propose a novel framework for representing image sets by approximating infinite-dimensional CovDs in the paradigm of the Nystr\"om method based on a Riemannian kernel.
Component SPD Matrices: A lower-dimensional discriminative data descriptor for image set classification
In the domain of pattern recognition, using the SPD (Symmetric Positive Definite) matrices to represent data and taking the metrics of resulting Riemannian manifold into account have been widely used for the task of image set classification.
Multiple Manifolds Metric Learning with Application to Image Set Classification
In image set classification, a considerable advance has been made by modeling the original image sets by second order statistics or linear subspace, which typically lie on the Riemannian manifold.
MindID: Person Identification from Brain Waves through Attention-based Recurrent Neural Network
The proposed approach is evaluated over 3 datasets (two local and one public).
Human-Computer Interaction
