چکیده (انگلیسی):

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.