Grassmannian Discriminant Maps (GDM) for Manifold Dimensionality Reduction with Application to Image Set Classification

TL;DR

This paper introduces Grassmannian Discriminant Maps (GDM), a novel method for manifold dimensionality reduction that enhances discriminative power in image set classification tasks across various domains.

Contribution

The paper proposes a new discriminant function for Grassmannian manifold dimensionality reduction, improving feature discrimination over existing methods.

Findings

GDM outperforms state-of-the-art methods in face recognition.

GDM improves object categorization accuracy.

GDM enhances hand gesture recognition performance.

Abstract

In image set classification, a considerable progress has been made by representing original image sets on Grassmann manifolds. In order to extend the advantages of the Euclidean based dimensionality reduction methods to the Grassmann Manifold, several methods have been suggested recently which jointly perform dimensionality reduction and metric learning on Grassmann manifold to improve performance. Nevertheless, when applied to complex datasets, the learned features do not exhibit enough discriminatory power. To overcome this problem, we propose a new method named Grassmannian Discriminant Maps (GDM) for manifold dimensionality reduction problems. The core of the method is a new discriminant function for metric learning and dimensionality reduction. For comparison and better understanding, we also study a simple variations to GDM. The key difference between them is the discriminant function. We experiment on data sets corresponding to three tasks: face recognition, object categorization, and hand gesture recognition to evaluate the proposed method and its simple extensions. Compared with the state of the art, the results achieved show the effectiveness of the proposed algorithm.