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 …

In this paper, a novel local covariance matrix (CM) representation method is proposed to
fully characterize the correlation among different spectral bands and the spatial-contextual …

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 …

The camera provides rich visual information regarding objects and becomes one of the most
mainstream sensors in the automation community. However, it is often difficult to be …

Representing images and videos with Symmetric Positive Definite (SPD) matrices, and
considering the Riemannian geometry of the resulting space, has been shown to yield high …

Visual tracking using multiple features has been proved as a robust approach because
features could complement each other. Since different types of variations such as …

This paper introduces a new nonlinear dictionary learning method for histograms in the
probability simplex. The method leverages optimal transport theory, in the sense that our aim …

Visual classification has attracted considerable research interests in the past decades. In
this paper, a novel ℓ 1-hypergraph model for visual classification is proposed. Hypergraph …

Similarity measure is fundamental to many machine learning and data mining algorithms.
Predefined similarity metrics are often data-dependent and sensitive to noise. Recently, data …

Data encoded as symmetric positive definite (SPD) matrices frequently arise in many areas
of computer vision and machine learning. While these matrices form an open subset of the …