Hashing with binary autoencoders

TL;DR

This paper introduces a binary autoencoder approach for image hashing that simplifies optimization and achieves competitive or superior retrieval performance compared to existing methods.

Contribution

The paper presents a novel binary autoencoder model for hashing, using auxiliary coordinates to simplify optimization and improve image retrieval accuracy.

Findings

Outperforms or matches state-of-the-art hashing methods

Simplifies optimization via auxiliary coordinates

Effective in image retrieval tasks

Abstract

An attractive approach for fast search in image databases is binary hashing, where each high-dimensional, real-valued image is mapped onto a low-dimensional, binary vector and the search is done in this binary space. Finding the optimal hash function is difficult because it involves binary constraints, and most approaches approximate the optimization by relaxing the constraints and then binarizing the result. Here, we focus on the binary autoencoder model, which seeks to reconstruct an image from the binary code produced by the hash function. We show that the optimization can be simplified with the method of auxiliary coordinates. This reformulates the optimization as alternating two easier steps: one that learns the encoder and decoder separately, and one that optimizes the code for each image. Image retrieval experiments, using precision/recall and a measure of code utilization, show the resulting hash function outperforms or is competitive with state-of-the-art methods for binary hashing.