Weighting video information into a multikernel SVM for human action recognition

Jordi Bautista-Ballester; Jaume Vergés-Llahí; Domenec Puig

doi:10.1117/12.2228527

8 December 2015 Weighting video information into a multikernel SVM for human action recognition

Jordi Bautista-Ballester, Jaume Vergés-Llahí, Domenec Puig

Proceedings Volume 9875, Eighth International Conference on Machine Vision (ICMV 2015); 98750J (2015) https://doi.org/10.1117/12.2228527
Event: Eighth International Conference on Machine Vision, 2015, Barcelona, Spain

Abstract

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.

Citation Download Citation

Jordi Bautista-Ballester, Jaume Vergés-Llahí, and Domenec Puig "Weighting video information into a multikernel SVM for human action recognition", Proc. SPIE 9875, Eighth International Conference on Machine Vision (ICMV 2015), 98750J (8 December 2015); https://doi.org/10.1117/12.2228527

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KEYWORDS

Databases

Video

RGB color model

Matrices

Cameras

Image classification

Visualization

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