A Generic Framework for Assessing the Performance Bounds of Image Feature Detectors

TL;DR

This paper introduces a generic framework based on repeatability measures to evaluate and compare the performance bounds of image feature detectors under various transformations, aiding in the design of more reliable vision systems.

Contribution

It presents a new generic framework for assessing the performance bounds of feature detectors, incorporating statistical significance testing and operating regions, applied to multiple transformations and detectors.

Findings

Identified performance differences among detectors under JPEG, lighting, and blurring transformations.

Established operating and guarantee regions for several state-of-the-art detectors.

Provided new insights into detector behavior using a large, diverse image database.

Abstract

Since local feature detection has been one of the most active research areas in computer vision during the last decade, a large number of detectors have been proposed. The interest in feature-based applications continues to grow and has thus rendered the task of characterizing the performance of various feature detection methods an important issue in vision research. Inspired by the good practices of electronic system design, a generic framework based on the repeatability measure is presented in this paper that allows assessment of the upper and lower bounds of detector performance and finds statistically significant performance differences between detectors as a function of image transformation amount by introducing a new variant of McNemars test in an effort to design more reliable and effective vision systems. The proposed framework is then employed to establish operating and guarantee regions for several state-of-the-art detectors and to identify their statistical performance differences for three specific image transformations: JPEG compression, uniform light changes and blurring. The results are obtained using a newly acquired, large image database (20482) images with 539 different scenes. These results provide new insights into the behaviour of detectors and are also useful from the vision systems design perspective.