Maximum entropy-based optimal threshold selection using deterministic reinforcement learning with controlled randomization

doi:10.1016/S0165-1684(02)00203-7

Return

Maximum entropy-based optimal threshold selection using deterministic reinforcement learning with controlled randomization

2002-07-01

PRE

Peng-Yeng Yin

DOI：10.1016/S0165-1684(02)00203-7

Original

Original request for help

Save

Abstract

En 中文

Traditional maximum entropy-based thresholding methods are very popular and efficient in the case of bilevel thresholding. But they are very computationally expensive when extended to multilevel thresholding since the inevitable exhaustive search of optimal thresholds needed to maximize the posterior entropy. In this paper, a reinforcement learning (R-L) approach is proposed for the maximum entropy thresholding. We show that finding the optimal thresholds using the maximum entropy criterion is equivalent to learning an optimal policy of the RL problem. Therefore, the powerful Q-learning algorithm. which is widely used in RL, can be employed to eradicate the computation burden of the maximum entropy-based thresholding methods. The experimental results show that the proposed method is suitable in the case of multilevel thresholding and the performance is better than that of the genetic algorithm-based entropy thresholding method. (C) 2002 Published by Elsevier Science B.V.

Keywords:

genetic algorithms

image segmentation

maximum entropy criterion

multilevel thresholding

Q-learning algorithm

reinforcement learning

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.