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Multi-level image thresholding by synergetic differential evolution
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DOI：10.1016/j.asoc.2013.11.018.png)
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Abstract
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The multi-level image thresholding is often treated as a problem of optimization. Typically, finding the parameters of these problems leads to a nonlinear optimization problem, for which obtaining the solutionis computationally expensive and time-consuming. In this paper a new multi-level image thresholding technique using synergetic differential evolution (SDE), an advanced version of differential evolution(DE), is proposed. SDE is a fusion of three algorithmic concepts proposed in modified versions of DE. It utilizes two criteria (1) entropy and (2) approximation of normalized histogram of an image by a mixture of Gaussian distribution to find the optimal thresholds. The experimental results show that SDE can make optimal thresholding applicable in case of multi-level thresholding and the performance is better than some other multi-level thresholding methods. (C) 2013 Elsevier B. V. All rights reserved.
Keywords:
Image segmentation
Optimization
Entropy
Gaussian curve fitting
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