@InProceedings{SouzaArJrCoNaKeGu:2016:DeNuFe,
author = "Souza, Kleber Jacques de and Araujo, Arnaldo de Albuquerque and
Jr, Zenilton Kleber G. do Patrociinio and Cousty, Jean and Najman,
Laurent and Kenmochi, Yukiko and Guimaraes, Silvio Jamil F.",
affiliation = "NPDI/DCC/UFMG - Federal University of Minas Gerais - Computer
Science Department - Belo Horizonte, MG, Brazil and NPDI/DCC/UFMG
- Federal University of Minas Gerais - Computer Science Department
- Belo Horizonte, MG, Brazil and {Audio-Visual Information Proc.
Lab. (VIPLAB) - Computer Science Department -- ICEI -- PUC Minas}
and Universite Paris-Est, Laboratoire d'Informatique Gaspard-Monge
UMR 8049, UPEMLV, ESIEE Paris, ENPC, CNRS, F-93162 Noisy-le-Grand
France and Universite Paris-Est, Laboratoire d'Informatique
Gaspard-Monge UMR 8049, UPEMLV, ESIEE Paris, ENPC, CNRS, F-93162
Noisy-le-Grand France and Universite Paris-Est, Laboratoire
d'Informatique Gaspard-Monge UMR 8049, UPEMLV, ESIEE Paris, ENPC,
CNRS, F-93162 Noisy-le-Grand France and {Audio-Visual Information
Proc. Lab. (VIPLAB) - Computer Science Department -- ICEI -- PUC
Minas}",
title = "Decreasing the Number of Features for Improving Human Action
Classification",
booktitle = "Proceedings...",
year = "2016",
editor = "Aliaga, Daniel G. and Davis, Larry S. and Farias, Ricardo C. and
Fernandes, Leandro A. F. and Gibson, Stuart J. and Giraldi, Gilson
A. and Gois, Jo{\~a}o Paulo and Maciel, Anderson and Menotti,
David and Miranda, Paulo A. V. and Musse, Soraia and Namikawa,
Laercio and Pamplona, Mauricio and Papa, Jo{\~a}o Paulo and
Santos, Jefersson dos and Schwartz, William Robson and Thomaz,
Carlos E.",
organization = "Conference on Graphics, Patterns and Images, 29. (SIBGRAPI)",
publisher = "IEEE Computer Society´s Conference Publishing Services",
address = "Los Alamitos",
keywords = "Spatio-temporal video segmentation, human action classification,
BossaNova representation.",
abstract = "Action classification in videos has been a very active field of
research over the past years. Human action classification is a
research field with application to various areas such as video
indexing, surveillance, human-computer interfaces, among others.
In this paper, we propose a strategy based on decreasing the
number of features in order to improve accuracy in the human
action classification task. Thus, to classify human action, we
firstly compute a video segmentation for simplifying the visual
information, in the following, we use a mid-level representation
for representing the feature vectors which are finally classified.
Experimental results demonstrate that our approach has improved
the quality of human action classification in comparison to the
baseline while using 60% less features.",
conference-location = "S{\~a}o Jos{\'e} dos Campos, SP, Brazil",
conference-year = "4-7 Oct. 2016",
doi = "10.1109/SIBGRAPI.2016.035",
url = "http://dx.doi.org/10.1109/SIBGRAPI.2016.035",
language = "en",
ibi = "8JMKD3MGPAW/3M5KB8P",
url = "http://urlib.net/ibi/8JMKD3MGPAW/3M5KB8P",
targetfile = "PID4373569.pdf",
urlaccessdate = "2024, Apr. 29"
}