Approximate Nearest Neighbor (ANN) methods such as Locality Sensitive Hashing, Semantic Hashing, and Spectral Hashing, provide computationally efficient procedures for finding 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 first method considers the Riemannian geometry of the manifold and performs Spectral Hashing in the tangent space of the manifold at several points. The second method divides the data into subsets and takes advantage of the
to perform non-Euclidean Spectral Hashing. For a data set of
samples the proposed methods are able to retrieve similar objects in as low as
) time complexity, where
is the number of clusters in the data. Since
, our methods are extremely efficient. We test and evaluate our methods on synthetic data generated from the Unit Hypersphere and the Grassmann manifold. Finally, we show promising results on a human action database.