@InProceedings{MonicaPi{\~n}ol2012,
author="Monica Pi{\~n}ol
and Angel Sappa
and Ricardo Toledo",
title="MultiTable Reinforcement for Visual Object Recognition",
booktitle="4th International Conference on Signal and Image Processing",
year="2012",
publisher="Springer India",
volume="221",
pages="469--480",
abstract="This paper presents a bag of feature based method for visual object recognition. Our contribution is focussed on the selection of the best feature descriptor. It is implemented by using a novel multi-table reinforcement learning method that selects among five of classical descriptors (i.e., Spin, SIFT, SURF, C-SIFT and PHOW) the one that best describes each image. Experimental results and comparisons are provided showing the improvements achieved with the proposed approach.",
optnote="ADAS",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=2157), last updated on Tue, 18 Oct 2016 13:32:09 +0200",
isbn="978-81-322-0996-6",
issn="1876-1100",
doi="10.1007/978-81-322-0997-3_42",
file=":http://refbase.cvc.uab.es/files/PST2012b.pdf:PDF"
}