# HPatches: A benchmark and evaluation of handcrafted and learned local descriptors

## TL;DR

This paper introduces a new benchmark and dataset for evaluating local image descriptors, addressing ambiguities in previous protocols and enabling more reliable comparisons of handcrafted and learned descriptors.

## Contribution

It provides a comprehensive, well-defined evaluation protocol and a large dataset for training and testing local descriptors, improving consistency and realism in performance assessment.

## Key findings

- Traditional descriptors can be improved with simple normalization.
- Deep learning descriptors do not always outperform handcrafted ones.
- Evaluation protocols significantly impact reported performance.

## Abstract

In this paper, we propose a novel benchmark for evaluating local image descriptors. We demonstrate that the existing datasets and evaluation protocols do not specify unambiguously all aspects of evaluation, leading to ambiguities and inconsistencies in results reported in the literature. Furthermore, these datasets are nearly saturated due to the recent improvements in local descriptors obtained by learning them from large annotated datasets. Therefore, we introduce a new large dataset suitable for training and testing modern descriptors, together with strictly defined evaluation protocols in several tasks such as matching, retrieval and classification. This allows for more realistic, and thus more reliable comparisons in different application scenarios. We evaluate the performance of several state-of-the-art descriptors and analyse their properties. We show that a simple normalisation of traditional hand-crafted descriptors can boost their performance to the level of deep learning based descriptors within a realistic benchmarks evaluation.