Performance Characterization of Image Feature Detectors in Relation to the Scene Content Utilizing a Large Image Database

TL;DR

This paper investigates how scene content influences the performance of local image feature detectors, using a large database to analyze their repeatability under various transformations.

Contribution

It introduces a new framework to determine scene types that maximize or minimize detector performance, filling a gap in existing evaluation methods.

Findings

Scene content significantly affects detector repeatability.

Performance varies across different types of scenes.

Insights into detector behavior under real-world conditions.

Abstract

Selecting the most suitable local invariant feature detector for a particular application has rendered the task of evaluating feature detectors a critical issue in vision research. Although the literature offers a variety of comparison works focusing on performance evaluation of image feature detectors under several types of image transformations, the influence of the scene content on the performance of local feature detectors has received little attention so far. This paper aims to bridge this gap with a new framework for determining the type of scenes which maximize and minimize the performance of detectors in terms of repeatability rate. The results are presented for several state-of-the-art feature detectors that have been obtained using a large image database of 20482 images under JPEG compression, uniform light and blur changes with 539 different scenes captured from real-world scenarios. These results provide new insights into the behavior of feature detectors.