Sparse matrix based hashing for approximate nearest neighbor search

Abstract

Binary hashing has been widely studied for approximate nearest neighbor (ANN) search with its compact representation and efficient comparison. Many existing hashing methods aim at improving the accuracy of ANN search, but ignore the complexity of generating binary codes. In this paper, we propose a new unsupervised hashing method based on a sparse matrix, named as Sparse Matrix based Hashing (SMH). There are only three kinds of elements in our sparse matrix, i.e., +1, −1 and 0.We learn the sparse matrix by optimizing a new pair-wise distancepreserving objective, in which the linear projection on the Euclidean distance and the corresponding Hamming distance is preserved. With the special form of the sparse matrix, the optimization can be solved by a greedy algorithm. The experiments on two large-scale datasets demonstrate that SMH expedites the process of generating binary codes, and achieves competitive performance with the state-of-the-art unsupervised hashing methods.