# Is Hamming distance the only way for matching binary image feature descriptors?

arXiv: https://arxiv.org/abs/1512.02355 · HTML: https://mlsanity.com/paper/1512.02355/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2015.

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

- [Analysis of Interpolation based Image In-painting Approaches](https://mlsanity.com/paper/2102.06564/) (2021)

## Abstract (arXiv)

Brute force matching of binary image feature descriptors is conventionally performed using the Hamming distance. This paper assesses the use of alternative metrics in order to see whether they can produce feature correspondences that yield more accurate homography matrices. Two statistical tests, namely ANOVA (Analysis of Variance) and McNemar's test were employed for evaluation. Results show that Jackard-Needham and Dice metrics can display better performance for some descriptors. Yet, these performance differences were not found to be statistically significant.