Bayesian Self-Organization

Part of Advances in Neural Information Processing Systems 6 (NIPS 1993)

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Alan L. Yuille, Stelios Smirnakis, Lei Xu


Recent work by Becker and Hinton (Becker and Hinton, 1992) shows a promising mechanism, based on maximizing mutual in(cid:173) formation assuming spatial coherence, by which a system can self(cid:173) organize itself to learn visual abilities such as binocular stereo. We introduce a more general criterion, based on Bayesian probability theory, and thereby demonstrate a connection to Bayesian theo(cid:173) ries of visual perception and to other organization principles for early vision (Atick and Redlich, 1990). Methods for implementa(cid:173) tion using variants of stochastic learning are described and, for the special case of linear filtering, we derive an analytic expression for the output.