Geometry-Aware Graph Embedding Projection Metric Learning for Image Set Classification

Abstract

By describing image sets as linear subspaces on the Grassmann manifold, image set classification has received persistent attention. Despite the success made so far, the unhelpfully intraclass diversity and interclass similarity remain two key challenges in finding an effective lower dimensional feature space for similarity measurement. To explore a feasible solution to these issues, we propose a geometry-aware graph embedding projection metric learning (GEPML) algorithm. The proposed approach first constructs the interclass and the intraclass similarity graphs on the Grassmann manifold, aiming to exploit the local structural information of the data manifold. Besides, we generalize the Euclidean collaborative representation mechanism to the Grassmann manifold to adaptively perform graph learning. Then, to learn the embedding mapping and the similarity metric jointly, we formulate the Grassmannian dimensionality reduction (GDR) problem into an elaborately designed metric learning regularization term. The proposed algorithm is appraised on five benchmarking data sets and the competitive experimental results demonstrate its feasibility and effectiveness.