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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
Conference or Workshop Item, Bibliographie

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.

Item Type: Conference or Workshop Item
Erschienen: 2012
Creators: Mousselly-Sergieh, Hatem ; Egyed-Zsigmond, Elöd ; Döller, Mario ; Coquil, David ; Pinon, Jean-Marie ; Kosch, Harald
Type of entry: Bibliographie
Title: Improving SURF Image Matching Using Supervised Learning
Language: English
Date: 2012
Publisher: IEEE Computer Society
Book Title: Proceedings of the 2012 Eighth International Conference on Signal Image Technology and Internet Based Systems
Series: SITIS '12
DOI: 10.1109/SITIS.2012.42
URL / URN: https://ieeexplore.ieee.org/document/6395100/
Corresponding Links:
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.

Uncontrolled Keywords: SURF, Image Matching, Classification
Identification Number: TUD-CS-2012-0373
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Ubiquitous Knowledge Processing
Date Deposited: 31 Dec 2016 14:29
Last Modified: 21 Sep 2018 10:09
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