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arXiv · 1604.07335

Scalable Gaussian Processes for Supervised Hashing

Bahadir Ozdemir·Larry S. Davis
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Abstract

We propose a flexible procedure for large-scale image search by hash functions with kernels. Our method treats binary codes and pairwise semantic similarity as latent and observed variables, respectively, in a probabilistic model based on Gaussian processes for binary classification. We present an efficient inference algorithm with the sparse pseudo-input Gaussian process (SPGP) model and parallelization. Experiments on three large-scale image dataset demonstrate the effectiveness of the proposed hashing method, Gaussian Process Hashing (GPH), for short binary codes and the datasets without predefined classes in comparison to the state-of-the-art supervised hashing methods.

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Bahadir Ozdemir, Larry S. Davis. 2016-04-25. Scalable Gaussian Processes for Supervised Hashing. https://arxiv.org/abs/1604.07335

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