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BMCV
2000
Springer

Unsupervised Learning of Biologically Plausible Object Recognition Strategies

11 years 11 months ago
Unsupervised Learning of Biologically Plausible Object Recognition Strategies
Recent psychological and neurological evidence suggests that biological object recognition is a process of matching sensed images to stored iconic memories. This paper presents a partial implementation of our interpretation of Kosslyn's biological vision model, with a control system added to it. We then show how reinforcement learning can be used to control and optimize recognition in an unsupervised learning mode, where the result of image matching is used as the reward signal to optimize earlier stages of processing.
Bruce A. Draper, Kyungim Baek
Added 02 Aug 2010
Updated 02 Aug 2010
Type Conference
Year 2000
Where BMCV
Authors Bruce A. Draper, Kyungim Baek
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