# Object Detection based Deep Unsupervised Hashing

arXiv: https://arxiv.org/abs/1811.09822 · HTML: https://mlsanity.com/paper/1811.09822/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2018.

## Built on by (most cited first, 3 of 3)

- [Unsupervised Hashing with Semantic Concept Mining](https://mlsanity.com/paper/2209.11475/) (2022)
- [Deep Self-Adaptive Hashing for Image Retrieval](https://mlsanity.com/paper/2108.07094/) (2021)
- [Distribution-Consistency-Guided Multi-modal Hashing](https://mlsanity.com/paper/2412.11216/) (2024)

## Abstract (arXiv)

Recently, similarity-preserving hashing methods have been extensively studied for large-scale image retrieval. Compared with unsupervised hashing, supervised hashing methods for labeled data have usually better performance by utilizing semantic label information. Intuitively, for unlabeled data, it will improve the performance of unsupervised hashing methods if we can first mine some supervised semantic 'label information' from unlabeled data and then incorporate the 'label information' into the training process. Thus, in this paper, we propose a novel Object Detection based Deep Unsupervised Hashing method (ODDUH). Specifically, a pre-trained object detection model is utilized to mining supervised 'label information', which is used to guide the learning process to generate high-quality hash codes.Extensive experiments on two public datasets demonstrate that the proposed method outperforms the state-of-the-art unsupervised hashing methods in the image retrieval task.