Covariance discriminative learning: A natural and efficient approach to image set classification
We propose a novel discriminative learning approach to image set classification by
modeling the image set with its natural second-order statistic, ie covariance matrix. Since …
modeling the image set with its natural second-order statistic, ie covariance matrix. Since …
Log-euclidean metric learning on symmetric positive definite manifold with application to image set classification
Abstract The manifold of Symmetric Positive Definite (SPD) matrices has been successfully
used for data representation in image set classification. By endowing the SPD manifold with …
used for data representation in image set classification. By endowing the SPD manifold with …
Fashionbert: Text and image matching with adaptive loss for cross-modal retrieval
In this paper, we address the text and image matching in cross-modal retrieval of the fashion
industry. Different from the matching in the general domain, the fashion matching is required …
industry. Different from the matching in the general domain, the fashion matching is required …
Projection metric learning on Grassmann manifold with application to video based face recognition
In video based face recognition, great success has been made by representing videos as
linear subspaces, which typically lie in a special type of non-Euclidean space known as …
linear subspaces, which typically lie in a special type of non-Euclidean space known as …
Grassmann discriminant analysis: a unifying view on subspace-based learning
In this paper we propose a discriminant learning framework for problems in which data
consist of linear subspaces instead of vectors. By treating subspaces as basic elements, we …
consist of linear subspaces instead of vectors. By treating subspaces as basic elements, we …
Kernel methods on Riemannian manifolds with Gaussian RBF kernels
In this paper, we develop an approach to exploiting kernel methods with manifold-valued
data. In many computer vision problems, the data can be naturally represented as points on …
data. In many computer vision problems, the data can be naturally represented as points on …
Heterogeneous cross-company defect prediction by unified metric representation and CCA-based transfer learning
Cross-company defect prediction (CCDP) learns a prediction model by using training data
from one or multiple projects of a source company and then applies the model to the target …
from one or multiple projects of a source company and then applies the model to the target …
Bregman divergence-based regularization for transfer subspace learning
The regularization principals [31] lead approximation schemes to deal with various learning
problems, eg, the regularization of the norm in a reproducing kernel Hilbert space for the ill …
problems, eg, the regularization of the norm in a reproducing kernel Hilbert space for the ill …
Statistical computations on Grassmann and Stiefel manifolds for image and video-based recognition
P Turaga, A Veeraraghavan… - … on Pattern Analysis …, 2011 - ieeexplore.ieee.org
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 …
underlying models have a special structure-the linear subspace structure. We discuss how …
A system for traffic sign detection, tracking, and recognition using color, shape, and motion information
C Bahlmann, Y Zhu, V Ramesh… - IEEE Proceedings …, 2005 - ieeexplore.ieee.org
This paper describes a computer vision based system for real-time robust traffic sign
detection, tracking, and recognition. Such a framework is of major interest for driver …
detection, tracking, and recognition. Such a framework is of major interest for driver …