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A hierarchical Bayesian model of invariant pattern recognition in the visual cortex (2005)
| Venue: | In Proceedings of the International Joint Conference on Neural Networks. IEEE |
| Citations: | 69 - 2 self |
BibTeX
@INPROCEEDINGS{George05ahierarchical,
author = {Dileep George},
title = {A hierarchical Bayesian model of invariant pattern recognition in the visual cortex},
booktitle = {In Proceedings of the International Joint Conference on Neural Networks. IEEE},
year = {2005},
pages = {1812--1817}
}
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Abstract
Abstract — We describe a hierarchical model of invariant visual pattern recognition in the visual cortex. In this model, the knowledge of how patterns change when objects move is learned and encapsulated in terms of high probability sequences at each level of the hierarchy. Configuration of object parts is captured by the patterns of coincident high probability sequences. This knowledge is then encoded in a highly efficient Bayesian Network structure.The learning algorithm uses a temporal stability criterion to discover object concepts and movement patterns. We show that the architecture and algorithms are biologically plausible. The large scale architecture of the system matches the large scale organization of the cortex and the micro-circuits derived from the local computations match the anatomical data on cortical circuits. The system exhibits invariance across a wide variety of transformations and is robust in the presence of noise. Moreover, the model also offers alternative explanations for various known cortical phenomena. I.
Keyphrases
visual cortex hierarchical bayesian model invariant pattern recognition anatomical data various known cortical phenomenon temporal stability criterion cortical circuit invariant visual pattern recognition large scale organization hierarchical model large scale architecture wide variety efficient bayesian network structure alternative explanation movement pattern object part object concept coincident high probability sequence high probability sequence local computation
