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Poster
An ensemble diversity approach to supervised binary hashing
Miguel A. Carreira-Perpinan · Ramin Raziperchikolaei

Wed Dec 07 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #54
in Wednesday Posters »

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.

Keywords: Combinatorial Optimization · (Other) Optimization · Similarity and Distance Learning · (Application) Information Retrieval · Nonlinear Dimension Reduction and Manifold Learning · Ensemble Methods and Boosting

Author Information

Miguel A. Carreira-Perpinan (UC Merced)
Ramin Raziperchikolaei (UC Merced)

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