# Fast Online Hashing with Multi-Label Projection

arXiv: https://arxiv.org/abs/2212.03112 · HTML: https://mlsanity.com/paper/2212.03112/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 28 references that are in the map.
- First posted on arXiv 3 Dec 2022.

## Builds on (most cited first, 8 of 8)

- [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)
- [MIHash: Online Hashing with Mutual Information](https://mlsanity.com/paper/1703.08919/) (2017)
- [Hadamard Matrix Guided Online Hashing](https://mlsanity.com/paper/1905.04454/) (2019)
- [Online Hashing](https://mlsanity.com/paper/1704.01897/) (2017)
- [Towards Optimal Discrete Online Hashing with Balanced Similarity](https://mlsanity.com/paper/1901.10185/) (2019)
- [Measuring the Accuracy of Object Detectors and Trackers](https://mlsanity.com/paper/1704.07293/) (2017)
- [Supervised Online Hashing via Similarity Distribution Learning](https://mlsanity.com/paper/1905.13382/) (2019)

## Abstract (arXiv)

Hashing has been widely researched to solve the large-scale approximate nearest neighbor search problem owing to its time and storage superiority. In recent years, a number of online hashing methods have emerged, which can update the hash functions to adapt to the new stream data and realize dynamic retrieval. However, existing online hashing methods are required to update the whole database with the latest hash functions when a query arrives, which leads to low retrieval efficiency with the continuous increase of the stream data. On the other hand, these methods ignore the supervision relationship among the examples, especially in the multi-label case. In this paper, we propose a novel Fast Online Hashing (FOH) method which only updates the binary codes of a small part of the database. To be specific, we first build a query pool in which the nearest neighbors of each central point are recorded. When a new query arrives, only the binary codes of the corresponding potential neighbors are updated. In addition, we create a similarity matrix which takes the multi-label supervision information into account and bring in the multi-label projection loss to further preserve the similarity among the multi-label data. The experimental results on two common benchmarks show that the proposed FOH can achieve dramatic superiority on query time up to 6.28 seconds less than state-of-the-art baselines with competitive retrieval accuracy.