Isometric hashing for image retrieval

Abstract

Hashing has been attracting much attention in computer vision recently, since it can provide efficient similarity comparison in massive multimedia databases with fast query speed and low storage cost. Since the distance metric is an explicit description of similarity, in this paper, a novel hashing method is proposed for image retrieval, dubbed Isometric Hashing (IH). IH aims to minimize the difference between the distance in input space and the distance of the corresponding binary codes. To tackle the discrete optimization in a computationally tractable manner, IH adopts some mathematical tricks to transform the original problem into a multi-objective optimization problem. The usage of linear-projection-based hash functions enables efficient generating hash codes for unseen data points. Furthermore, utilizing different distance metrics could produce corresponding hashing algorithms, thus IH can be seen as a framework for developing new hashing methods. Extensive experiments performed on four benchmark datasets validate that IH can achieve comparable to or even better results than some state-of-the-art hashing methods.