TY - GEN
T1 - Comparative evaluation of binary features
AU - Heinly, Jared
AU - Dunn, Enrique
AU - Frahm, Jan Michael
PY - 2012
Y1 - 2012
N2 - Performance evaluation of salient features has a long-standing tradition in computer vision. In this paper, we fill the gap of evaluation for the recent wave of binary feature descriptors, which aim to provide robustness while achieving high computational efficiency. We use established metrics to embed our assessment into the body of existing evaluations, allowing us to provide a novel taxonomy unifying both traditional and novel binary features. Moreover, we analyze the performance of different detector and descriptor pairings, which are often used in practice but have been infrequently analyzed. Additionally, we complement existing datasets with novel data testing for illumination change, pure camera rotation, pure scale change, and the variety present in photo-collections. Our performance analysis clearly demonstrates the power of the new class of features. To benefit the community, we also provide a website for the automatic testing of new description methods using our provided metrics and datasets ( www.cs.unc.edu/feature-evaluation ).
AB - Performance evaluation of salient features has a long-standing tradition in computer vision. In this paper, we fill the gap of evaluation for the recent wave of binary feature descriptors, which aim to provide robustness while achieving high computational efficiency. We use established metrics to embed our assessment into the body of existing evaluations, allowing us to provide a novel taxonomy unifying both traditional and novel binary features. Moreover, we analyze the performance of different detector and descriptor pairings, which are often used in practice but have been infrequently analyzed. Additionally, we complement existing datasets with novel data testing for illumination change, pure camera rotation, pure scale change, and the variety present in photo-collections. Our performance analysis clearly demonstrates the power of the new class of features. To benefit the community, we also provide a website for the automatic testing of new description methods using our provided metrics and datasets ( www.cs.unc.edu/feature-evaluation ).
KW - binary features
KW - comparison
KW - evaluation
UR - http://www.scopus.com/inward/record.url?scp=84867865899&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84867865899&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-33709-3_54
DO - 10.1007/978-3-642-33709-3_54
M3 - Conference contribution
AN - SCOPUS:84867865899
SN - 9783642337086
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 759
EP - 773
BT - Computer Vision, ECCV 2012 - 12th European Conference on Computer Vision, Proceedings
T2 - 12th European Conference on Computer Vision, ECCV 2012
Y2 - 7 October 2012 through 13 October 2012
ER -