TY  - CONF
AU  - Monica Piñol
AU  - Angel Sappa
AU  - Ricardo Toledo
A2  - ICSIP
PY  - 2012//
TI  - MultiTable Reinforcement for Visual Object Recognition
T2  - LNCS
BT  - 4th International Conference on Signal and Image Processing
SP  - 469
EP  - 480
VL  - 221
PB  - Springer India
N2  - 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.
SN  - 1876-1100
SN  - 978-81-322-0996-6
L1  - http://refbase.cvc.uab.es/files/PST2012b.pdf
UR  - http://dx.doi.org/10.1007/978-81-322-0997-3_42
N1  - ADAS
ID  - Monica Piñol2012
ER  -