PT Unknown
AU Monica Piñol
   Angel Sappa
   Ricardo Toledo
TI MultiTable Reinforcement for Visual Object Recognition
BT 4th International Conference on Signal and Image Processing
PY 2012
BP 469
EP 480
VL 221
DI 10.1007/978-81-322-0997-3_42
AB 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.
ER