Technical Note: \cal Q-Learning
Machine Learning
A reinforcement learning model of selective visual attention
Proceedings of the fifth international conference on Autonomous agents
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Unsupervised Learning of Models for Recognition
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Object Recognition Using Local Information Content
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Rapid object recognition from discriminative regions of interest
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
The computational neuroscience of visual cognition: attention, memory and reward
WAPCV'04 Proceedings of the Second international conference on Attention and Performance in Computational Vision
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The innovation of this work is the provision of a system that learns visual encodings of attention patterns and that enables sequential attention for object detection in real world environments. The system embeds the saccadic decision procedure in a cascaded process where visual evidence is probed at the most informative image locations. It is based on the extraction of information theoretic saliency by determining informative local image descriptors that provide selected foci of interest. Both the local information in terms of code book vector responses, and the geometric information in the shift of attention contribute to the recognition state of a Markov decision process. A Q-learner performs then explorative search on useful actions towards salient locations, developing a strategy of useful action sequences being directed in state space towards the optimization of information maximization. The method is evaluated in experiments on real world object recognition and demonstrates efficient performance in outdoor tasks.