  Binary hashing is a well-known approach for fast approximate nearest-neighbor
search in information retrieval. Much work has focused on affinity-based
objective functions involving the hash functions or binary codes. These
objective functions encode neighborhood information between data points and are
often inspired by manifold learning algorithms. They ensure that the hash
functions differ from each other through constraints or penalty terms that
encourage codes to be orthogonal or dissimilar across bits, but this couples
the binary variables and complicates the already difficult optimization. We
propose a much simpler approach: we train each hash function (or bit)
independently from each other, but introduce diversity among them using
techniques from classifier ensembles. Surprisingly, we find that not only is
this faster and trivially parallelizable, but it also improves over the more
complex, coupled objective function, and achieves state-of-the-art precision
and recall in experiments with image retrieval.
