TY - GEN
T1 - Growing a bag of systems tree for fast and accurate classification
AU - Coviello, Emanuele
AU - Mumtaz, Adeel
AU - Chan, Antoni B.
AU - Lanckriet, Gert R. G.
PY - 2012
Y1 - 2012
N2 - The bag-of-systems (BoS) representation is a descriptor of motion in a video, where dynamic texture (DT) codewords represent the typical motion patterns in spatio-temporal patches extracted from the video. The efficacy of the BoS descriptor depends on the richness of the codebook, which directly depends on the number of codewords in the codebook. However, for even modest sized codebooks, mapping videos onto the codebook results in a heavy computational load. In this paper we propose the BoS Tree, which constructs a bottom-up hierarchy of codewords that enables efficient mapping of videos to the BoS codebook. By leveraging the tree structure to efficiently index the codewords, the BoS Tree allows for fast look-ups in the codebook and enables the practical use of larger, richer codebooks. We demonstrate the effectiveness of BoS Trees on classification of three video datasets, as well as on annotation of a music dataset. © 2012 IEEE.
AB - The bag-of-systems (BoS) representation is a descriptor of motion in a video, where dynamic texture (DT) codewords represent the typical motion patterns in spatio-temporal patches extracted from the video. The efficacy of the BoS descriptor depends on the richness of the codebook, which directly depends on the number of codewords in the codebook. However, for even modest sized codebooks, mapping videos onto the codebook results in a heavy computational load. In this paper we propose the BoS Tree, which constructs a bottom-up hierarchy of codewords that enables efficient mapping of videos to the BoS codebook. By leveraging the tree structure to efficiently index the codewords, the BoS Tree allows for fast look-ups in the codebook and enables the practical use of larger, richer codebooks. We demonstrate the effectiveness of BoS Trees on classification of three video datasets, as well as on annotation of a music dataset. © 2012 IEEE.
UR - http://www.scopus.com/inward/record.url?scp=84866636161&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84866636161&origin=recordpage
U2 - 10.1109/CVPR.2012.6247900
DO - 10.1109/CVPR.2012.6247900
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781467312264
SP - 1979
EP - 1986
BT - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
T2 - 2012 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012
Y2 - 16 June 2012 through 21 June 2012
ER -