@InProceedings{EloiPuertas2014,
author="Eloi Puertas
and Miguel Angel Bautista
and Daniel Sanchez
and Sergio Escalera
and Oriol Pujol",
title="Learning to Segment Humans by Stacking their Body Parts,",
booktitle="ECCV Workshop on ChaLearn Looking at People",
year="2014",
volume="8925",
pages="685--697",
optkeywords="Human body segmentation",
optkeywords="Stacked Sequential Learning",
abstract="Human segmentation in still images is a complex task due to the wide range of body poses and drastic changes in environmental conditions. Usually, human body segmentation is treated in a two-stage fashion. First, a human body part detection step is performed, and then, human part detections are used as prior knowledge to be optimized by segmentation strategies. In this paper, we present a two-stage scheme based on Multi-Scale Stacked Sequential Learning (MSSL). We define an extended feature set by stacking a multi-scale decomposition of bodypart likelihood maps. These likelihood maps are obtained in a first stageby means of a ECOC ensemble of soft body part detectors. In a second stage, contextual relations of part predictions are learnt by a binary classifier, obtaining an accurate body confidence map. The obtained confidence map is fed to a graph cut optimization procedure to obtain the final segmentation. Results show improved segmentation when MSSL is included in the human segmentation pipeline.",
optnote="HuPBA;MILAB",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=2553), last updated on Tue, 18 Oct 2016 11:48:56 +0200",
doi="10.1007/978-3-319-16178-5_48",
file=":http://refbase.cvc.uab.es/files/PBS2014.pdf:PDF"
}