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Evaluation Method, Dataset Size or Dataset Content: How to Evaluate Algorithms for Image Matching?

Kanwal, Nadia and Bostanci, Erkan and Clark, Adrian F (2016) 'Evaluation Method, Dataset Size or Dataset Content: How to Evaluate Algorithms for Image Matching?' Journal of Mathematical Imaging and Vision, 55 (3). pp. 378-400. ISSN 0924-9907

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Most vision papers have to include some evaluation work in order to demonstrate that the algorithm proposed is an improvement on existing ones. Generally, these evaluation results are presented in tabular or graphical forms. Neither of these is ideal because there is no indication as to whether any performance differences are statistically significant. Moreover, the size and nature of the dataset used for evaluation will obviously have a bearing on the results, and neither of these factors are usually discussed. This paper evaluates the effectiveness of commonly used performance characterization metrics for image feature detection and description for matching problems and explores the use of statistical tests such as McNemar’s test and ANOVA as better alternatives.

Item Type: Article
Uncontrolled Keywords: Performance characterization; Feature matching; Homography
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 08 Jun 2016 14:32
Last Modified: 23 Sep 2022 18:27

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