An ensemble diversity approach to supervised binary hashing
This addresses the challenge of complex optimization in binary hashing for information retrieval, offering a more efficient solution, though it is incremental as it builds on existing ensemble methods.
The paper tackled the problem of optimizing binary hashing for fast approximate nearest-neighbor search by proposing a simpler approach that trains hash functions independently using ensemble diversity techniques, resulting in improved speed, parallelizability, and state-of-the-art precision and recall in image retrieval experiments.
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.