Distance Preserving Marginal Hashing for image retrieval

Hashing for image retrieval has attracted lots of attentions in recent years due to its fast computational speed and storage efficiency. Many existing hashing methods obtain the hashing functions through mapping neighbor items to similar codes, while ignoring the non-neighbor items. One exception is the Local Linear Spectral Hashing (LLSH), which introduces negative values into the local affinity matrix to map non-neighbor images to non-similar codes. However, setting 10th percentile distance in affinity matrix as a threshold, which is used to judge neighbors and non-neighbors, is not reasonable. In this paper, we propose a novel unsupervised hashing method called Distance Preserving Marginal Hashing (DPMH) which not only makes the average Hamming distance minimized for the intra-cluster pairs and maximized for the inter-cluster pairs, but also preserves the distance of non-neighbor points. Furthermore, we adopt an efficient sequential procedure to learn the hashing functions. The experimental results on two large-scale benchmark datasets demonstrate the effectiveness and efficiency of our method over other state-of-the-art unsupervised methods.