# Unsupervised Triplet Hashing for Fast Image Retrieval

## TL;DR

This paper introduces Unsupervised Triplet Hashing (UTH), a novel CNN-based method that improves large-scale image retrieval by generating discriminative, high-entropy hash codes without labeled data, outperforming existing methods.

## Contribution

The paper presents a new unsupervised hashing approach tailored for image retrieval, emphasizing discriminative features, minimal quantization loss, and maximum entropy in hash codes.

## Key findings

- UTH outperforms state-of-the-art unsupervised hashing methods on CIFAR-10, MNIST, and In-shop datasets.
- The method achieves higher retrieval accuracy in large-scale image retrieval tasks.
- Extensive experiments validate the effectiveness of the proposed principles.

## Abstract

Hashing has played a pivotal role in large-scale image retrieval. With the development of Convolutional Neural Network (CNN), hashing learning has shown great promise. But existing methods are mostly tuned for classification, which are not optimized for retrieval tasks, especially for instance-level retrieval. In this study, we propose a novel hashing method for large-scale image retrieval. Considering the difficulty in obtaining labeled datasets for image retrieval task in large scale, we propose a novel CNN-based unsupervised hashing method, namely Unsupervised Triplet Hashing (UTH). The unsupervised hashing network is designed under the following three principles: 1) more discriminative representations for image retrieval; 2) minimum quantization loss between the original real-valued feature descriptors and the learned hash codes; 3) maximum information entropy for the learned hash codes. Extensive experiments on CIFAR-10, MNIST and In-shop datasets have shown that UTH outperforms several state-of-the-art unsupervised hashing methods in terms of retrieval accuracy.