Riemannian Kernel-based Hashing for Fast Image Set Classification

Image set classification has attracted extensive attention in recent years. Most existing image set classification methods depend on computationally complicated optimization, and the computational complexity and memory usage can be prohibitively expensive when dealing with large-scale datasets. Hashing is a widely-used technique in approximate nearest neighbour search due to its superiority in computation and storage. To take advantage of hashing, in this paper, we propose Riemannian Kernel-based Hashing for efficient image set classification. The proposed SPD-based Iterative Quantization (SPD-ITQ) and Grassmann-based Iterative Quantization (GM-ITQ) first model image set by SPD and Grassmann manifolds to capture nonlinear structure, respectively. The proposed methods map the points in Riemannian space into Hilbert space, and further learn high-quality hash code by learning a rotation matrix to minimize quantization error. Extensive experiments on four image set datasets verify efficiency and competitive performance of the proposed methods compared with the state-of-the-art methods.