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We propose a flexible procedure for large-scale image search by hashfunctions with kernels. Our method treats binary codes and pairwise semanticsimilarity as latent and observed variables, respectively, in a probabilisticmodel based on Gaussian processes for binary classification. We present anefficient inference algorithm with the sparse pseudo-input Gaussian process(SPGP) model and parallelization. Experiments on three large-scale imagedataset demonstrate the effectiveness of the proposed hashing method, GaussianProcess Hashing (GPH), for short binary codes and the datasets withoutpredefined classes in comparison to the state-of-the-art supervised hashingmethods.

Scalable Gaussian Processes for Supervised Hashing