Multiple Manifolds Metric Learning with Application to Image Set Classification

TL;DR

This paper introduces a novel metric learning approach that combines multiple Riemannian manifolds using kernels to improve image set classification, achieving state-of-the-art results on face recognition and object categorization datasets.

Contribution

It proposes a new algorithm that fuses multiple Riemannian manifolds via kernels and learns a common subspace for enhanced image set classification.

Findings

Achieved state-of-the-art accuracy on three datasets.

Effectively fuses multiple manifolds for better feature representation.

Demonstrates robustness across different classification tasks.

Abstract

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. Specifically, they are Symmetric Positive Definite (SPD) manifold and Grassmann manifold respectively, and some algorithms have been developed on them for classification tasks. Motivated by the inability of existing methods to extract discriminatory features for data on Riemannian manifolds, we propose a novel algorithm which combines multiple manifolds as the features of the original image sets. In order to fuse these manifolds, the well-studied Riemannian kernels have been utilized to map the original Riemannian spaces into high dimensional Hilbert spaces. A metric Learning method has been devised to embed these kernel spaces into a lower dimensional common subspace for classification. The state-of-the-art results achieved on three datasets corresponding to two different classification tasks, namely face recognition and object categorization, demonstrate the effectiveness of the proposed method.