Unsupervised Ensemble Hashing: Boosting Minimum Hamming Distance

Hashing aims at learning discriminative binary codes of high-dimensional data for the approximate nearest neighbor searching. However, the distance ranking obtained by traditional methods is not optimum in the Hamming space, and it degrades the performance for retrieval tasks. To tackle the above problem, an unsupervised ensemble hashing is proposed to improve the ranking accuracy by assembling the diverse hash tables independently in this paper. We observe that the higher the accuracy is the larger diversity the base learner has, and the more effective the ensemble method is. Based on this principle, two special ensembles hashing approaches are proposed to increase diversity by bootstrap sampling with data-dependent methods. Especially, the results are better when the minimum Hamming distance is large and the variance of the Hamming distance is small. This proposed method is conducted in the experiments and the results show that it can achieve about 10%-25% performance compared with the baseline algorithm, which achieves competitive results with the state-of-the-art methods on the CIFAR-10 and LabelMe benchmarks.