Performance Characterization of Image Feature Detectors in Relation to the Scene Content Utilizing a Large Image Database
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 detec...
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doaj-dcad26aaf0954f378b4944ba1044895b2021-03-29T20:36:44ZengIEEEIEEE Access2169-35362018-01-0168564857310.1109/ACCESS.2018.27954608263204Performance Characterization of Image Feature Detectors in Relation to the Scene Content Utilizing a Large Image DatabaseBruno Ferrarini0https://orcid.org/0000-0002-3657-4466Shoaib Ehsan1https://orcid.org/0000-0001-9631-1898Ales Leonardis2Naveed Ur Rehman3Klaus D. McDonald-Maier4School of Computer Science and Electronic Engineering, University of Essex, Colchester, U.K.School of Computer Science and Electronic Engineering, University of Essex, Colchester, U.K.School of Computer Science, University of Birmingham, Birmingham, U.K.Department of Electrical Engineering, COMSATS Institute of Information Technology, Islamabad, PakistanSchool of Computer Science and Electronic Engineering, University of Essex, Colchester, U.K.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.https://ieeexplore.ieee.org/document/8263204/Feature extractionimage analysisperformance analysisfeature detectorcomparisonrepeatability |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Bruno Ferrarini Shoaib Ehsan Ales Leonardis Naveed Ur Rehman Klaus D. McDonald-Maier |
spellingShingle |
Bruno Ferrarini Shoaib Ehsan Ales Leonardis Naveed Ur Rehman Klaus D. McDonald-Maier Performance Characterization of Image Feature Detectors in Relation to the Scene Content Utilizing a Large Image Database IEEE Access Feature extraction image analysis performance analysis feature detector comparison repeatability |
author_facet |
Bruno Ferrarini Shoaib Ehsan Ales Leonardis Naveed Ur Rehman Klaus D. McDonald-Maier |
author_sort |
Bruno Ferrarini |
title |
Performance Characterization of Image Feature Detectors in Relation to the Scene Content Utilizing a Large Image Database |
title_short |
Performance Characterization of Image Feature Detectors in Relation to the Scene Content Utilizing a Large Image Database |
title_full |
Performance Characterization of Image Feature Detectors in Relation to the Scene Content Utilizing a Large Image Database |
title_fullStr |
Performance Characterization of Image Feature Detectors in Relation to the Scene Content Utilizing a Large Image Database |
title_full_unstemmed |
Performance Characterization of Image Feature Detectors in Relation to the Scene Content Utilizing a Large Image Database |
title_sort |
performance characterization of image feature detectors in relation to the scene content utilizing a large image database |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2018-01-01 |
description |
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. |
topic |
Feature extraction image analysis performance analysis feature detector comparison repeatability |
url |
https://ieeexplore.ieee.org/document/8263204/ |
work_keys_str_mv |
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_version_ |
1724194521132040192 |