Fast Approximate Nearest Neighbor Methods for Non-Euclidean Manifolds with Applications to Human Activity Analysis in Videos

Approximate Nearest Neighbor (ANN) methods such as Locality Sensitive Hashing, Semantic Hashing, and Spectral Hashing, provide computationally ecient procedures for nding objects similar to a query object in large datasets. These methods have been successfully applied to search web-scale datasets that can contain millions of images. Unfortunately, the key assumption in these procedures is that objects in the dataset lie in a Euclidean space. This assumption is not always valid and poses a challenge for several computer vision applications where data commonly lies in complex non-Euclidean manifolds. In particular, dynamic data such as human activities are commonly represented as distributions over bags of video words or as dynamical systems. In this paper, we propose two new algorithms that extend Spectral Hashing to non-Euclidean spaces. The rst method considers the Riemannian geometry of the manifold and performs Spectral Hashing in the tangent space of the manifold at several poi...

• Type: Conference
• Year: 2010
• Where: ECCV
• Authors: Rizwan Chaudhry, Yuri Ivanov
• Keywords: Activity Analysis In Videos, Approximate Nearest Neighbors, ECCV 2010, Hashing, Non-Euclidean Manifolds