Prototype-Based Sample Selection for Active Hashing

Several hashing-based methods for Approximate Nearest Neighbors (ANN) search in a large data set have been proposed recently. In particular, semi-supervised hashing utilizes semantic similarity given for a small fraction of pairwise data samples and active hashing aims to improve the performance for ANN search by relying on an expert for the labeling of the most informative points. In this study, we present an active hashing method by prototype-based sample selection. Knowing semantic similarities between cluster prototypes can help extracting relations among the points in the corresponding clusters. For expert labeling, we select prototypes from clusters which do not contain any data points with labeled information so that all areas can be covered effectively. Experimental results demonstrate that the proposed active hashing method improves the performance for ANN search.