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Learning to parse images

Hinton, GE; Ghahramani, Z; Teh, YW; (2000) Learning to parse images. In: Solla, SA and Leen, TK and Muller, KR, (eds.) ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 12. (pp. 463 - 469). M I T PRESS

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Abstract

We describe a class of probabilistic models that we call credibility networks. Using parse trees as internal representations of images, credibility networks are able to perform segmentation and recognition simultaneously, removing the need for ad hoc segmentation heuristics. Promising results in the problem of segmenting handwritten digits were obtained.

Type:Proceedings paper
Title:Learning to parse images
Event:13th Annual Conference on Neural Information Processing Systems (NIPS)
Location:CO
Dates:1999-11-29 - 1999-12-04
ISBN:0-262-19450-3
Keywords:LIKELIHOOD, ALGORITHM, NETWORKS
UCL classification:UCL > School of Life and Medical Sciences > Faculty of Life Sciences > Gatsby Computational Neuroscience Unit

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