An ensemble diversity approach to supervised binary hashing

Binary hashing is a well-known approach for fast approximat e nearest-neighbor search in information retrieval. Much work has focused on af finity-based objective functions involving the hash functions or binary codes. The se objective functions encode neighborhood information between data points and ar e often inspired by manifold learning algorithms. They ensure that the hash fun ctions differ from each other through constraints or penalty terms that encourage c odes to be orthogonal or dissimilar across bits, but this couples the binary varia bles and complicates the already difficult optimization. W e propose a much simpler ap proach: we train each hash function (or bit) independently from each other, b ut introduce diversity among them using techniques from classifier ensembles. Surp risingly, we find that not only is this faster and trivially parallelizable, b ut it also improves over the more complex, coupled objective function, and achieves sta te-of-the-art precision and recall in experiments with image retrieval.