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Remote Sens. 2016, 8(11), 928;

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

School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK
School of Computer Science, University of Birmingham, Birmingham B15 2TT, UK
Electrical Engineering Department, COMSATS Institute of Information Technology, Islamabad 44000, Pakistan
College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan
This paper is an extended version of our paper published in Assessing the Performance Bounds of Local Feature Detectors: Taking Inspiration From Electronics Design Practices. In Proceedings of the 2015 International Conference on Systems, Signals and Image Processing (IWSSIP), London, UK, 10–12 September 2015.
Author to whom correspondence should be addressed.
Academic Editors: Lizhe Wang, Guoqing Zhou, Richard Müller and Prasad S. Thenkabail
Received: 20 May 2016 / Revised: 24 October 2016 / Accepted: 4 November 2016 / Published: 9 November 2016
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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. View Full-Text
Keywords: local feature detection; evaluation framework; performance analysis local feature detection; evaluation framework; performance analysis

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Ehsan, S.; Clark, A.F.; Leonardis, A.; ur Rehman, N.; Khaliq, A.; Fasli, M.; McDonald-Maier, K.D. A Generic Framework for Assessing the Performance Bounds of Image Feature Detectors. Remote Sens. 2016, 8, 928.

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