Research Repository

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

Ehsan, Shoaib and Clark, Adrian and Leonardis, Ales and ur Rehman, Naveed and Khaliq, Ahmad and Fasli, Maria and McDonald-Maier, Klaus (2016) 'A Generic Framework for Assessing the Performance Bounds of Image Feature Detectors.' Remote Sensing, 8 (11). ISSN 2072-4292

[img]
Preview
Text
remotesensing-08-00928.pdf - Published Version
Available under License Creative Commons Attribution.

Download (27MB) | Preview

Abstract

Since local feature detection has been one of the most active research areas in computer vision during the last decade and has found wide range of applications (such as matching and registration of remotely sensed image data), 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 McNemar’s 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 (20,482 images) with 539 different scenes. These results provide new insights into the behavior of detectors and are also useful from the vision systems design perspective. Finally, results for some local feature detectors are presented for a set of remote sensing images to showcase the potential and utility of this framework for remote sensing applications in general.

Item Type: Article
Uncontrolled Keywords: local feature detection; evaluation framework; performance analysis
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Jim Jamieson
Date Deposited: 07 Nov 2016 11:09
Last Modified: 29 Jun 2018 13:15
URI: http://repository.essex.ac.uk/id/eprint/17892

Actions (login required)

View Item View Item