In this paper we present an unsupervised algorithm to select the most adequate grouping of regions in an image using a hierarchical clustering scheme. Then, we introduce an optimisation approach for the whole process. The grouping method presented is based on the maximisation of a measure that represents the perceptual decision. The whole strategy takes profit from a hierarchical clustering to find a maximum of the proposed criterion. The algorithm has been used to segment real images as well as multispectral images achieving very accurate results on this task. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Martínez-Usó, A., Pla, F., & García-Sevilla, P. (2006). Unsupervised image segmentation using a hierarchical clustering selection process. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4109 LNCS, pp. 799–807). Springer Verlag. https://doi.org/10.1007/11815921_88
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