Abstract

In many challenging visual recognition tasks where training data is limited, Vectors of Locally Aggregated Descriptors (VLAD) have emerged as powerful image/video representations that compete with or outperform state-ofthe-art approaches. In this paper, we address two fundamental limitations of VLAD: its requirement for the local descriptors to have vector form and its restriction to linear classifiers due to its high-dimensionality. To this end, we introduce a kernelized version of VLAD. This not only lets us inherently exploit more sophisticated classification schemes, but also enables us to efficiently aggregate nonvector descriptors (e.g., manifold-valued data) in the VLAD framework. Furthermore, we propose an approximate formulation that allows us to accelerate the coding process while still benefiting from the properties of kernel VLAD. Our experiments demonstrate the effectiveness of our approach at handling manifold-valued data, such as covariance descriptors, on several classification tasks. Our re-sults also evidence the benefits of our nonlinear VLAD descriptors against the linear ones in Euclidean space using several standard benchmark datasets.