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. © 2013 Springer.
CITATION STYLE
Piñol, M., Sappa, A. D., & Toledo, R. (2013). Multi-table reinforcement learning for visual object recognition. In Lecture Notes in Electrical Engineering (Vol. 221 LNEE, pp. 469–479). https://doi.org/10.1007/978-81-322-0997-3_42
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