@InProceedings{JoseCarlosRubio2012,
author="Jose Carlos Rubio
and Joan Serrat
and Antonio Lopez",
title="Unsupervised co-segmentation through region matching",
booktitle="25th IEEE Conference on Computer Vision and Pattern Recognition",
year="2012",
publisher="IEEE Xplore",
pages="749--756",
abstract="Co-segmentation is defined as jointly partitioning multiple images depicting the same or similar object, into foreground and background. Our method consists of a multiple-scale multiple-image generative model, which jointly estimates the foreground and background appearance distributions from several images, in a non-supervised manner. In contrast to other co-segmentation methods, our approach does not require the images to have similar foregrounds and different backgrounds to function properly. Region matching is applied to exploit inter-image information by establishing correspondences between the common objects that appear in the scene. Moreover, computing many-to-many associations of regions allow further applications, like recognition of object parts across images. We report results on iCoseg, a challenging dataset that presents extreme variability in camera viewpoint, illumination and object deformations and poses. We also show that our method is robust against large intra-class variability in the MSRC database.",
optnote="ADAS",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=2033), last updated on Mon, 14 Oct 2013 14:26:24 +0200",
isbn="978-1-4673-1226-4",
issn="1063-6919",
doi="10.1109/CVPR.2012.6247745",
file=":http://refbase.cvc.uab.es/files/rsl2012c.pdf:PDF"
}