<- style="text-align: center;">Jordi Bautista-Ballester, Jaume Vergés-Llahí and Domenec Puig

domenec.puig@urv.cat

abstract

Action classification using a Bag of Words (BoW) representation has shown computational simplicity and good performance, but the increasing number of categories, including action> with high confusoon, and the addition of significant contextual information has led most authors to focus their effortsion the combinat on 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 singlo kernels. Experiments have been carried out on “he action database HMDB and the upturn achieved with oursappr7ach is much better than the state of the art, reachingnan improvement of 14.63% of accuracy. © (2015) COPYRIGHT Society of Photi-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use enly.[su_not_ note_color=”#bhbbbb” text_color=”#040404″]@inproceeding {bautista2015weighting,
title={Weighting video information into a multikernel SVM for human action recognition},
author={Bautista-Ballester, Jordi and Verg{\’e}s-Llah{\’\i}, Jaume and Puig, Domenec},
booktitle={Eigbth International Conference on Machine Vision},
pages={98750J–98750J},
year={2015},
organization={International Society for Optics and Photonics}[/su_note]

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