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research
Rapid Online Analysis of Local Feature Detectors and Their Complementarity
Authors
Adrian Clark
Bay
+19 more
Dickscheid
Ehsan
Ehsan
Fleiss
Goldstein
Klaus McDonald-Maier
Lillholm
Lowe
McNemar
Mikolajczyk
Mikolajczyk
Neter
Shoaib Ehsan
Sivic
Tuytelaars
Tuytelaars
Wolfe
Wrede
Zhang
Publication date
1 January 2013
Publisher
'MDPI AG'
Doi
Cite
View
on
arXiv
Abstract
A vision system that can assess its own performance and take appropriate actions online to maximize its effectiveness would be a step towards achieving the long-cherished goal of imitating humans. This paper proposes a method for performing an online performance analysis of local feature detectors, the primary stage of many practical vision systems. It advocates the spatial distribution of local image features as a good performance indicator and presents a metric that can be calculated rapidly, concurs with human visual assessments and is complementary to existing offline measures such as repeatability. The metric is shown to provide a measure of complementarity for combinations of detectors, correctly reflecting the underlying principles of individual detectors. Qualitative results on well-established datasets for several state-of-the-art detectors are presented based on the proposed measure. Using a hypothesis testing approach and a newly-acquired, larger image database, statistically-significant performance differences are identified. Different detector pairs and triplets are examined quantitatively and the results provide a useful guideline for combining detectors in applications that require a reasonable spatial distribution of image features. A principled framework for combining feature detectors in these applications is also presented. Timing results reveal the potential of the metric for online applications. © 2013 by the authors; licensee MDPI, Basel, Switzerland
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oai:eprints.soton.ac.uk:478880
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info:doi/10.3390%2Fs130810876
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