Action classification using a Bag of Words (BoW) representation has shown computational simplicity and good performance, but the increasing number of categories, including actions with high confusion, and the addition of significant contextual information has led most authors to focus their efforts on the combination of image descriptors. In this approach we code the action videos using a BoW representation with diverse image descriptors and introduce them to the optimal SVM kernel as a linear combination of learning weighted single kernels. Experiments have been carried out on the action database HMDB and the upturn achieved with our approach is much better than the state of the art, reaching an improvement of 14.63% of accuracy.

Date Published: 8 December 2015
PDF: 5 pages
Proc. SPIE 9875, Eighth International Conference on Machine Vision (ICMV 2015), 98750J (8 December 2015); doi: 10.1117/12.2228527

Published in SPIE Proceedings Vol. 9875:
Eighth International Conference on Machine Vision (ICMV 2015)
Antanas Verikas; Petia Radeva; Dmitry Nikolaev, Editor(s)