Abstract
Image segmentation is an important process in image processing. Clustering-based image segmentation algorithms have a number of advantages such as continuous contour and non-threshold. However, most of the clustering-based image segmentation algorithms may occur an oversegmentation problem or need numerous control parameters depending on image. In this paper, a non-parametric clustering-based image segmentation algorithm using an orthogonal experimental design based hill-climbing is proposed. For solving the oversegmentation problem, a general-purpose evaluation function is used in the algorithm. Experimental results of natural images demonstrate the effectiveness of the proposed algorithm.
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Lee, KZ., Chuang, WC., Ho, SY. (2003). A Non-parametric Image Segmentation Algorithm Using an Orthogonal Experimental Design Based Hill-Climbing. In: Liu, J., Cheung, Ym., Yin, H. (eds) Intelligent Data Engineering and Automated Learning. IDEAL 2003. Lecture Notes in Computer Science, vol 2690. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45080-1_154
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DOI: https://doi.org/10.1007/978-3-540-45080-1_154
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40550-4
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