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/ |
| Zugehörige Links: | |
| 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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