# Enhance Feature Discrimination for Unsupervised Hashing

## TL;DR

This paper presents Gemb, a Gaussian Mixture Model embedding technique that enhances feature discrimination in unsupervised hashing, leading to improved performance across multiple benchmark datasets.

## Contribution

The paper introduces Gemb, a novel embedding method that boosts feature discriminability and enhances existing unsupervised hashing algorithms.

## Key findings

- Gemb improves hashing performance on benchmark datasets.
- Enhanced feature discrimination leads to better retrieval accuracy.
- Compatible with multiple state-of-the-art hashing methods.

## Abstract

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.