@InProceedings{AlmeidaJrGuim:2017:CaStHu,
author = "Almeida, Raquel and Jr, Zenilton K. Goncalves do Patrocinio and
Guimaraes, Silvio Jamil Ferzoli",
affiliation = "{PUC Minas} and {PUC Minas} and {PUC Minas}",
title = "A New Pooling Strategy based on Local Feature Distribution: A Case
Study for Human Action Classification",
booktitle = "Proceedings...",
year = "2017",
editor = "Torchelsen, Rafael Piccin and Nascimento, Erickson Rangel do and
Panozzo, Daniele and Liu, Zicheng and Farias, Myl{\`e}ne and
Viera, Thales and Sacht, Leonardo and Ferreira, Nivan and Comba,
Jo{\~a}o Luiz Dihl and Hirata, Nina and Schiavon Porto, Marcelo
and Vital, Creto and Pagot, Christian Azambuja and Petronetto,
Fabiano and Clua, Esteban and Cardeal, Fl{\'a}vio",
organization = "Conference on Graphics, Patterns and Images, 30. (SIBGRAPI)",
publisher = "IEEE Computer Society",
address = "Los Alamitos",
keywords = "Mid-level representation, Human action classification, Pooling.",
abstract = "Mid-level representations are used to map sets of local features
into one global representation for a given media descriptor. In
visual pattern recognition tasks, Bag-of-Words (BoW) is one
popular strategy, among many methods available in literature, due
mainly by the simplicity in concept and implementation. Despite
the overall good results achieved by BoW in many tasks, the method
is unstable in high dimensional feature space and quantization
errors are usually ignored in the final representation. To cope
with these problems, we propose a new pooling function based on
feature points distribution around codewords. We propose to use
the standard deviation associated with each codeword to measure
attribution discrepancy and weight the impact that feature points
will assume in the final representation. The main contribution of
this article is the study of more discriminative representations,
which amplify values of feature points close to codewords border
regions. Experiments were conducted in human action classification
task and results demonstrated that our pooling strategy has
improved the classification rates in 25.6% for UCF Sports dataset
and 21.4% for UCF 11 dataset, with respect to the original pooling
function used in BoW.",
conference-location = "Niter{\'o}i, RJ, Brazil",
conference-year = "17-20 Oct. 2017",
doi = "10.1109/SIBGRAPI.2017.26",
url = "http://dx.doi.org/10.1109/SIBGRAPI.2017.26",
language = "en",
ibi = "8JMKD3MGPAW/3PHKBHE",
url = "http://urlib.net/ibi/8JMKD3MGPAW/3PHKBHE",
targetfile = "PID4982925.pdf",
urlaccessdate = "2024, Apr. 29"
}