Two-Pass K Nearest Neighbor Search for Feature Tracking
In recent years, feature tracking has become one of the most important research topics in computer vision. Many efforts have been made to design excellent feature matching methods. For large-scale structure from motion, however, existing feature tracking methods still need to improve in aspects of s...
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| Format: | Article |
| Language: | English |
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IEEE
2018-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8528423/ |
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| author | Mingwei Cao Wei Jia Zhihan Lv Wenjun Xie Liping Zheng Xiaoping Liu |
| author_facet | Mingwei Cao Wei Jia Zhihan Lv Wenjun Xie Liping Zheng Xiaoping Liu |
| author_sort | Mingwei Cao |
| collection | DOAJ |
| description | In recent years, feature tracking has become one of the most important research topics in computer vision. Many efforts have been made to design excellent feature matching methods. For large-scale structure from motion, however, existing feature tracking methods still need to improve in aspects of speed and matching confidence. To defense the drawbacks, in this paper, we design a simple and efficient feature tracking method based on the standard <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> nearest neighbor search. First, the parallel scale-invariant feature transform (SIFT) is selected as the feature detector to locate keypoints. Second, the principal component analysis-based SIFT-descriptor extractor is used to compute robust descriptions for the selected keypoints, in which normalized operation is used for boosting the matching score. Third, the two-pass <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> nearest neighbor search (TP-KNN) is proposed to produce correspondences for image pairs, then leading a significant improvement in the number of matches. Moreover, a geometry-constraint approach is proposed to remove outliers from the initial matches for boosting the matching precision. Finally, we conduct experiment on several challenging benchmark datasets to assess the TP-KNN method against the state-of-the-art methods. Experimental results indicate that the TP-KNN has the best performance in both speed and accuracy. |
| format | Article |
| id | doaj-art-aef75f2976c0423c8375d8fcc48b53ae |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-aef75f2976c0423c8375d8fcc48b53ae2025-08-20T03:08:31ZengIEEEIEEE Access2169-35362018-01-016729397295110.1109/ACCESS.2018.28793378528423Two-Pass K Nearest Neighbor Search for Feature TrackingMingwei Cao0Wei Jia1Zhihan Lv2Wenjun Xie3https://orcid.org/0000-0002-4032-8049Liping Zheng4Xiaoping Liu5School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, ChinaSchool of Computer Science and Information Engineering, Hefei University of Technology, Hefei, ChinaSchool of Data Science and Software Engineering, Qingdao University, Qingdao, ChinaSchool of Computer Science and Information Engineering, Hefei University of Technology, Hefei, ChinaSchool of Computer Science and Information Engineering, Hefei University of Technology, Hefei, ChinaSchool of Computer Science and Information Engineering, Hefei University of Technology, Hefei, ChinaIn recent years, feature tracking has become one of the most important research topics in computer vision. Many efforts have been made to design excellent feature matching methods. For large-scale structure from motion, however, existing feature tracking methods still need to improve in aspects of speed and matching confidence. To defense the drawbacks, in this paper, we design a simple and efficient feature tracking method based on the standard <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> nearest neighbor search. First, the parallel scale-invariant feature transform (SIFT) is selected as the feature detector to locate keypoints. Second, the principal component analysis-based SIFT-descriptor extractor is used to compute robust descriptions for the selected keypoints, in which normalized operation is used for boosting the matching score. Third, the two-pass <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> nearest neighbor search (TP-KNN) is proposed to produce correspondences for image pairs, then leading a significant improvement in the number of matches. Moreover, a geometry-constraint approach is proposed to remove outliers from the initial matches for boosting the matching precision. Finally, we conduct experiment on several challenging benchmark datasets to assess the TP-KNN method against the state-of-the-art methods. Experimental results indicate that the TP-KNN has the best performance in both speed and accuracy.https://ieeexplore.ieee.org/document/8528423/Feature trackingK nearest neighbor3D reconstructionstructure from motionfeature matching |
| spellingShingle | Mingwei Cao Wei Jia Zhihan Lv Wenjun Xie Liping Zheng Xiaoping Liu Two-Pass K Nearest Neighbor Search for Feature Tracking IEEE Access Feature tracking K nearest neighbor 3D reconstruction structure from motion feature matching |
| title | Two-Pass K Nearest Neighbor Search for Feature Tracking |
| title_full | Two-Pass K Nearest Neighbor Search for Feature Tracking |
| title_fullStr | Two-Pass K Nearest Neighbor Search for Feature Tracking |
| title_full_unstemmed | Two-Pass K Nearest Neighbor Search for Feature Tracking |
| title_short | Two-Pass K Nearest Neighbor Search for Feature Tracking |
| title_sort | two pass k nearest neighbor search for feature tracking |
| topic | Feature tracking K nearest neighbor 3D reconstruction structure from motion feature matching |
| url | https://ieeexplore.ieee.org/document/8528423/ |
| work_keys_str_mv | AT mingweicao twopassknearestneighborsearchforfeaturetracking AT weijia twopassknearestneighborsearchforfeaturetracking AT zhihanlv twopassknearestneighborsearchforfeaturetracking AT wenjunxie twopassknearestneighborsearchforfeaturetracking AT lipingzheng twopassknearestneighborsearchforfeaturetracking AT xiaopingliu twopassknearestneighborsearchforfeaturetracking |