Local Linear Spectral Hashing

Kang Zhao, Dengxiang Liu, Hongtao Lu

Abstract: Hashing for large scale image retrieval has become more and more popular because of its improvement in computational speed and storage reduction. Spectral Hashing (SH) is a very efficient unsupervised hashing method through mapping similar images to similar binary codes. However, it doesn’t take the non-neighbor points into consideration, and its assumption of uniform data distribution is usually not true. In this paper, we propose a \(local\ linear\ spectral\ hashing\) framework that minimizes the average Hamming distance with a new local neighbor matrix, which can guarantee the mapping not only from neighbor images to neighbor codes, but also from non-neighbor images to non-neighbor codes. Based on the framework, we utilize three linear methods to handle the proposed problem, including orthogonal hashing, non-orthogonal hashing, and sequential hashing. The experiments on two huge datasets demonstrate the efficiency and accuracy of our methods.