# Hashing for Similarity Search: A Survey

arXiv: https://arxiv.org/abs/1408.2927 · HTML: https://mlsanity.com/paper/1408.2927/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Aug 2014.

## Built on by (most cited first, 30 of 34)

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- [For Manifold Learning, Deep Neural Networks can be Locality Sensitive Hash Functions](https://mlsanity.com/paper/2103.06875/) (2021)
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- [Cross-modal Zero-shot Hashing by Label Attributes Embedding](https://mlsanity.com/paper/2111.04080/) (2021)
- [Unsupervised Space Partitioning for Nearest Neighbor Search](https://mlsanity.com/paper/2206.08091/) (2022)
- [SImProv: Scalable Image Provenance Framework for Robust Content Attribution](https://mlsanity.com/paper/2206.14245/) (2022)
- [Synthetic Similarity Search in Automotive Production](https://mlsanity.com/paper/2505.07256/) (2025)
- [Hashing-Baseline: Rethinking Hashing in the Age of Pretrained Models](https://mlsanity.com/paper/2509.14427/) (2025)
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- [Utilizing Low-Dimensional Molecular Embeddings for Rapid Chemical Similarity Search](https://mlsanity.com/paper/2402.07970/) (2024)
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## Abstract (arXiv)

Similarity search (nearest neighbor search) is a problem of pursuing the data items whose distances to a query item are the smallest from a large database. Various methods have been developed to address this problem, and recently a lot of efforts have been devoted to approximate search. In this paper, we present a survey on one of the main solutions, hashing, which has been widely studied since the pioneering work locality sensitive hashing. We divide the hashing algorithms two main categories: locality sensitive hashing, which designs hash functions without exploring the data distribution and learning to hash, which learns hash functions according the data distribution, and review them from various aspects, including hash function design and distance measure and search scheme in the hash coding space.