Generative models and Bayesian model comparison for shape recognition

被引：7

作者：

Krishnapuram, B ^{[1
]}

Bishop, CM ^{[1
]}

Szummer, M ^{[1
]}

机构：

[1] Microsoft Res, Cambridge CB3 0FB, England

来源：

NINTH INTERNATIONAL WORKSHOP ON FRONTIERS IN HANDWRITING RECOGNITION, PROCEEDINGS | 2004年

关键词：

D O I：

10.1109/IWFHR.2004.46

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Recognition of hand-drawn shapes is an important and widely studied problem. By adopting a generative probabilistic framework we are able to formulate a robust and flexible approach to shape recognition which allows for a wide range of shapes and which can recognize new shapes from a single exemplar It also provides meaningful probabilistic measures of model score which can be used as part of a larger probabilistic framework for interpreting a page of ink. We also show how Bayesian model comparison allows the trade-off between data fit and model complexity to be optimized automatically.

引用

页码：20 / 25

页数：6

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