Multiple Manifolds Metric Learning with Application to Image Set Classification
This work addresses the challenge of improving classification accuracy in image set analysis for applications like face recognition and object categorization, representing an incremental advancement over existing manifold-based methods.
The paper tackled the problem of extracting discriminatory features for image set classification on Riemannian manifolds by proposing a novel algorithm that combines multiple manifolds as features, achieving state-of-the-art results on three datasets for face recognition and object categorization.
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.