A New Variational Method for Deep Supervised Semantic Image Hashing

Abstract

We present a supervised semantic hashing method which uses a variational autoencoder to represent each database image sample as a product Bernoulli distribution. We show that the probability parameters approach extreme values during training, allowing them to be used directly as hash bits. We show how our method allows balanced bits to be directly specified, and is superior to state-of-the-art methods across four datasets.