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Improving SURF Image Matching Using Supervised Learning

Mousselly-Sergieh, Hatem ; Egyed-Zsigmond, Elöd ; Döller, Mario ; Coquil, David ; Pinon, Jean-Marie ; Kosch, Harald (2012)
Improving SURF Image Matching Using Supervised Learning.
doi: 10.1109/SITIS.2012.42
Konferenzveröffentlichung, Bibliographie

Kurzbeschreibung (Abstract)

Key points-based image matching algorithms have proven very successful in recent years. However, their execution time makes them unsuitable for online applications. Indeed, identifying similar key points requires comparing a large number of high dimensional descriptor vectors. Previous work has shown that matching could be still accurately performed when only considering a few highly significant key points. In this paper, we investigate reducing the number of generated SURF features to speed up image matching while maintaining the matching recall at a high level. We propose a machine learning approach that uses a binary classifier to identify key points that are useful for the matching process. Furthermore, we compare the proposed approach to another method for key point pruning based on saliency maps. The two approaches are evaluated using ground truth datasets. The evaluation shows that the proposed classification-based approach outperforms the adversary in terms of the trade-off between the matching recall and the percentage of reduced key points. Additionally, the evaluation demonstrates the ability of the proposed approach of effectively reducing the matching runtime.

Typ des Eintrags: Konferenzveröffentlichung
Erschienen: 2012
Autor(en): Mousselly-Sergieh, Hatem ; Egyed-Zsigmond, Elöd ; Döller, Mario ; Coquil, David ; Pinon, Jean-Marie ; Kosch, Harald
Art des Eintrags: Bibliographie
Titel: Improving SURF Image Matching Using Supervised Learning
Sprache: Englisch
Publikationsjahr: 2012
Verlag: IEEE Computer Society
Buchtitel: Proceedings of the 2012 Eighth International Conference on Signal Image Technology and Internet Based Systems
Reihe: SITIS '12
DOI: 10.1109/SITIS.2012.42
URL / URN: https://ieeexplore.ieee.org/document/6395100/
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Kurzbeschreibung (Abstract):

Key points-based image matching algorithms have proven very successful in recent years. However, their execution time makes them unsuitable for online applications. Indeed, identifying similar key points requires comparing a large number of high dimensional descriptor vectors. Previous work has shown that matching could be still accurately performed when only considering a few highly significant key points. In this paper, we investigate reducing the number of generated SURF features to speed up image matching while maintaining the matching recall at a high level. We propose a machine learning approach that uses a binary classifier to identify key points that are useful for the matching process. Furthermore, we compare the proposed approach to another method for key point pruning based on saliency maps. The two approaches are evaluated using ground truth datasets. The evaluation shows that the proposed classification-based approach outperforms the adversary in terms of the trade-off between the matching recall and the percentage of reduced key points. Additionally, the evaluation demonstrates the ability of the proposed approach of effectively reducing the matching runtime.

Freie Schlagworte: SURF, Image Matching, Classification
ID-Nummer: TUD-CS-2012-0373
Fachbereich(e)/-gebiet(e): 20 Fachbereich Informatik
20 Fachbereich Informatik > Ubiquitäre Wissensverarbeitung
Hinterlegungsdatum: 31 Dez 2016 14:29
Letzte Änderung: 21 Sep 2018 10:09
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