# Local Orthogonal-Group Testing

arXiv: https://arxiv.org/abs/1807.09848 · HTML: https://mlsanity.com/paper/1807.09848/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jul 2018.

## Built on by (most cited first, 1 of 1)

- [Group Testing for Accurate and Efficient Range-Based Near Neighbor Search for Plagiarism Detection](https://mlsanity.com/paper/2311.02573/) (2023)

## Abstract (arXiv)

This work addresses approximate nearest neighbor search applied in the domain of large-scale image retrieval. Within the group testing framework we propose an efficient off-line construction of the search structures. The linear-time complexity orthogonal grouping increases the probability that at most one element from each group is matching to a given query. Non-maxima suppression with each group efficiently reduces the number of false positive results at no extra cost. Unlike in other well-performing approaches, all processing is local, fast, and suitable to process data in batches and in parallel. We experimentally show that the proposed method achieves search accuracy of the exhaustive search with significant reduction in the search complexity. The method can be naturally combined with existing embedding methods.