By Topic

Learning in Gibbsian fields: how accurate and how fast can it be?

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Song-Chun Zhu ; Dept. of Comput. & Inf. Sci., Ohio State Univ., Columbus, OH, USA ; Xiuwen Liu

In this article, we present a unified framework for learning Gibbs models from training images. We identify two key factors that determine the accuracy and speed of learning Gibbs models: (1). Fisher information, and (2). The accuracy of Monte Carlo estimate for partition functions. We propose three new learning algorithms under the unified framework. (I). The maximum partial likelihood estimator. (II). The maximum patch likelihood estimator, and (III). The maximum satellite likelihood estimator. The first two algorithms can speed up the minimax entropy algorithm by about 2D times without losing much accuracy. The third one makes use of a set of known Gibbs models as references-dubbed “satellites” and can approximately estimate the minimax entropy model in the speed of 10 seconds

Published in:

Computer Vision and Pattern Recognition, 2000. Proceedings. IEEE Conference on  (Volume:2 )

Date of Conference:

2000