Scalable Gaussian Processes for Supervised Hashing

TL;DR

This paper introduces Gaussian Process Hashing (GPH), a scalable probabilistic approach for large-scale image retrieval that effectively generates binary codes based on semantic similarity, outperforming existing methods.

Contribution

It presents a novel probabilistic framework using Gaussian processes for supervised hashing, incorporating sparse pseudo-inputs and parallelization for scalability.

Findings

GPH outperforms state-of-the-art supervised hashing methods on large-scale datasets.

The method effectively generates short binary codes with high retrieval accuracy.

Scalable inference is achieved through the sparse pseudo-input Gaussian process model.

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.