%0 Conference Proceedings
%T MultiTable Reinforcement for Visual Object Recognition
%A Monica Piñol
%A Angel Sappa
%A Ricardo Toledo
%B 4th International Conference on Signal and Image Processing
%D 2012
%V 221
%I Springer India
%@ 1876-1100
%@ 978-81-322-0996-6
%F Monica Piñol2012
%O ADAS
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=2157), last updated on Tue, 18 Oct 2016 13:32:09 +0200
%X 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.
%U http://refbase.cvc.uab.es/files/PST2012b.pdf
%U http://dx.doi.org/10.1007/978-81-322-0997-3_42
%P 469-480