1 research outputs found
Enhance Feature Discrimination for Unsupervised Hashing
We introduce a novel approach to improve unsupervised hashing. Specifically,
we propose a very efficient embedding method: Gaussian Mixture Model embedding
(Gemb). The proposed method, using Gaussian Mixture Model, embeds feature
vector into a low-dimensional vector and, simultaneously, enhances the
discriminative property of features before passing them into hashing. Our
experiment shows that the proposed method boosts the hashing performance of
many state-of-the-art, e.g. Binary Autoencoder (BA) [1], Iterative Quantization
(ITQ) [2], in standard evaluation metrics for the three main benchmark
datasets.Comment: Accepted to ICIP 201