Neighborhood Pyramid Preserving Hashing

Abstract

In this paper, we devote our efforts to the approximate nearest neighbour (ANN) search problem and propose a new unsupervised binary hashing method, i.e., Neighbourhood Pyramid preserving Hashing (NPH). We represent the nearest neighbours of each data point in a pyramid, and as the learning objective, we impose that the pyramid neighbourhood in each level is consistently preserved across the original Euclidean space and the transformed Hamming space. The neighbourhood is quantitatively characterized by its size, defined as the average distance from the involved nearest neighbours to the referred data point. Our approach is consistent with the distance-preserving principle of binary hashing and achieves stricter neighbourhood structure preserving over previous graph hashing algorithms. The experiments on several large-scale benchmark datasets demonstrate that NPH achieves promising performances compared with those of the existing state-of-the-art unsupervised binary hashing methods.