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An Effective Multilevel Thresholding Approach Using Conditional Probability Entropy and Genetic Algorithm

Chang, Y. and Yan, H.

    Entropy-based image thresholding are used widely in image processing. Conventional methods are efficient in the case of bilevel thresholding. But they are very computationally time consuming when extended to multilevel thresholding since they exhaustively search the optimal thresholds to optimize the objective functions. In this paper, we propose a conditional probability entropy (CPE) based on Bayesian theory and employ Genetic Algorithm (GA) to maximize the CPE for the multithresholds. The experimental results show that CPE is a good criterion of image thresholding and GA is a applicable fast algorithm for multi-level thresholding compared to the exhaustive searching method.
Cite as: Chang, Y. and Yan, H. (2003). An Effective Multilevel Thresholding Approach Using Conditional Probability Entropy and Genetic Algorithm. In Proc. Pan-Sydney Area Workshop on Visual Information Processing (VIP2002), Sydney, Australia. CRPIT, 22. Jin, J. S., Eades, P., Feng, D. D. and Yan, H., Eds. ACS. 17.
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