PT Journal
AU Monica Piñol
   Angel Sappa
   Ricardo Toledo
TI Adaptive Feature Descriptor Selection based on a Multi-Table Reinforcement Learning Strategy
SO Neurocomputing
JI NEUCOM
PY 2015
BP 106–115
VL 150
IS A
DI 10.1016/j.neucom.2014.03.080
DE Reinforcement learning; Q-learning; Bag of features; Descriptors
AB This paper presents and evaluates a framework to improve the performance of visual object classification methods, which are based on the usage of image feature descriptors as inputs. The goal of the proposed framework is to learn the best descriptor for each image in a given database. This goal is reached by means of a reinforcement learning process using the minimum information. The visual classification system used to demonstrate the proposed framework is based on a bag of features scheme, and the reinforcement learning technique is implemented through the Q-learning approach. The behavior of the reinforcement learning with different state definitions is evaluated. Additionally, a method that combines all these states is formulated in order to select the optimal state. Finally, the chosen actions are obtained from the best set of image descriptors in the literature: PHOW, SIFT, C-SIFT, SURF and Spin. Experimental results using two public databases (ETH and COIL) are provided showing both the validity of the proposed approach and comparisons with state of the art. In all the cases the best results are obtained with the proposed approach.
ER