DocumentCode
3284096
Title
Evaluation of binary keypoint descriptors
Author
Bekele, D. ; Teutsch, Michael ; Schuchert, Tobias
Author_Institution
Karlsruhe Inst. of Technol., Karlsruhe, Germany
fYear
2013
fDate
15-18 Sept. 2013
Firstpage
3652
Lastpage
3656
Abstract
In this paper an evaluation of state-of-the-art binary keypoint descriptors, namely BRIEF, ORB, BRISK and FREAK, is presented. In contrast to previous evaluations we used the Stanford Mobile Visual Search (SMVS) data set because binary descriptors are mainly used in mobile applications. This large data set does provide a lot of characteristic transformations for mobile devices, but no ground truth data. The often used Oxford data set is used only for validation purposes. We use ratio-test and RANSAC (RANdom SAmple Consensus) for evaluation and present results for accuracy, precision and average number of best matches as performance metrics. The validity of the results is also checked by evaluating these binary keypoint descriptors on Oxford data set. The obtained results show that BRISK is the keypoint descriptor which gives highest percentage of precision and largest number of best matches among all the binary descriptors. Next to BRISK is FREAK, which offers comparably good result.
Keywords
feature extraction; image matching; image retrieval; mobile computing; object tracking; random processes; statistical testing; BRIEF; BRISK; FREAK; ORB; Oxford data set; RANSAC; SMVS data set; Stanford mobile visual search; binary keypoint descriptors; characteristic transformations; matching; mobile applications; mobile devices; mobile feature tracking; random sammple consensus; ratio-test; binary descriptors; evaluation; invariance; matching; mobile feature tracking; recognition;
fLanguage
English
Publisher
ieee
Conference_Titel
Image Processing (ICIP), 2013 20th IEEE International Conference on
Conference_Location
Melbourne, VIC
Type
conf
DOI
10.1109/ICIP.2013.6738753
Filename
6738753
Link To Document