# Online Hashing with Similarity Learning

arXiv: https://arxiv.org/abs/2108.02560 · HTML: https://mlsanity.com/paper/2108.02560/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 53 references that are in the map.
- First posted on arXiv 4 Jul 2021.

## Builds on (most cited first, 16 of 16)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [A Survey on Learning to Hash](https://mlsanity.com/paper/1606.00185/) (2016)
- [Deep Metric Learning with Angular Loss](https://mlsanity.com/paper/1708.01682/) (2017)
- [Learning to Hash for Indexing Big Data - A Survey](https://mlsanity.com/paper/1509.05472/) (2015)
- [Learning to Hash with Binary Deep Neural Network](https://mlsanity.com/paper/1607.05140/) (2016)
- [MIHash: Online Hashing with Mutual Information](https://mlsanity.com/paper/1703.08919/) (2017)
- [Supervised Online Hashing via Hadamard Codebook Learning](https://mlsanity.com/paper/1905.03694/) (2019)
- [Hadamard Matrix Guided Online Hashing](https://mlsanity.com/paper/1905.04454/) (2019)
- [Online Hashing](https://mlsanity.com/paper/1704.01897/) (2017)
- [The Power of Asymmetry in Binary Hashing](https://mlsanity.com/paper/1311.7662/) (2013)
- [Towards Optimal Discrete Online Hashing with Balanced Similarity](https://mlsanity.com/paper/1901.10185/) (2019)
- [Query-adaptive Image Retrieval by Deep Weighted Hashing](https://mlsanity.com/paper/1612.02541/) (2016)
- [Online Hashing with Efficient Updating of Binary Codes](https://mlsanity.com/paper/1911.12125/) (2019)
- [Fast Class-wise Updating for Online Hashing](https://mlsanity.com/paper/2012.00318/) (2020)
- [Efficient Querying from Weighted Binary Codes](https://mlsanity.com/paper/1912.05006/) (2019)

## Abstract (arXiv)

Online hashing methods usually learn the hash functions online, aiming to efficiently adapt to the data variations in the streaming environment. However, when the hash functions are updated, the binary codes for the whole database have to be updated to be consistent with the hash functions, resulting in the inefficiency in the online image retrieval process. In this paper, we propose a novel online hashing framework without updating binary codes. In the proposed framework, the hash functions are fixed and a parametric similarity function for the binary codes is learnt online to adapt to the streaming data. Specifically, a parametric similarity function that has a bilinear form is adopted and a metric learning algorithm is proposed to learn the similarity function online based on the characteristics of the hashing methods. The experiments on two multi-label image datasets show that our method is competitive or outperforms the state-of-the-art online hashing methods in terms of both accuracy and efficiency for multi-label image retrieval.