Local Descriptor Groupings in Reinforcement Learning of Sensory-Motor Attention
Publication from Digital
Paletta L., Fritz G.
POSTER at 29th European Conference on Visual Perception, ECVP 2006, abstract, St. Petersburg, Russia, August , 2006
Previous research on behavioural modelling of saccadic image interpretation
(Henderson, 1982 Psychological Science 8 51 - 55) has emphasised
the sampling of informative parts under visual attention to guide
visual perception. We propose a system of sequential attention for
object recognition that (i) groups n-tuples of local-gradient-based
image descriptors (Lowe, 2004 International Journal of Computer Vision
60 91 - 110) being scale-, rotation-, and to high degree illumination-tolerant,
defining a vocabulary of prototypical code descriptors, (ii) selects
only informative groupings for further processing, (iii) learns a
predictive mapping from a current perceptual state in a Markov decision
process to a next saccadic action, and (iv) present a model of object
recognition being capable of integrating sequential information by
minimisation of entropy in the Bayesian modeling of object hypotheses.
The innovative abstraction level of informative groupings provides
perceptual meta-states in sensory - motor attention, enabling the
learning of a purposeful grammar integrating atomic feature - saccade
mappings into a meaningful recognition behaviour. We demonstrate
highly accurate recognition of outdoor facades in a mobile vision
application, using the sensory - motor context of trans-saccadic
object recognition.