Bayesian Self-Organization

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.

Authors

• Alan Yuille Johns Hopkins University
• Stelios Smirnakis
• Lei Xu Institute of Cyber Science and Technology, Shanghai Jiao Tong University, Shanghai, China Shanghai Key Laboratory of Integrated Administration Technologies for Information Security, Shanghai, China

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