Scalable Gaussian Processes for Supervised Hashing
This work addresses scalable image retrieval for applications like search engines, though it appears incremental as it builds on existing Gaussian process and hashing techniques.
The paper tackles large-scale image search by developing Gaussian Process Hashing (GPH), a method that uses Gaussian processes for binary classification to generate hash codes, achieving effectiveness with short binary codes and datasets without predefined classes compared to state-of-the-art supervised hashing methods.
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.