Geometry and Topology Preserving Hashing for SIFT Feature

Abstract

In recent years, content-based image retrieval has been of concern because of practical needs on Internet services, especially methods that can improve retrieving speed and accuracy. The SIFT feature is a well-designed local feature. It has mature applications in feature matching and retrieval, whereas the raw SIFT feature is high dimensional, with high storage cost as well as computational cost in feature similarity measurements. Thus, we propose a hashing scheme for fast SIFT feature-based image matching and retrieval. First, a training process of the hashing function involves geometric and topological information being introduced; second, a geometry-enhanced similarity evaluation that considers both the global and details of images in evaluation is explained. Compared with state-of-the-art methods, our method achieves better performance.