Abstract
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.
This work has been partly supported by projects ESP2005-07724-C05-05 from Spanish CICYT and P1-1B2004-36 from Fundació Caixa-Castelló.
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Martínez-Usó, A., Pla, F., García-Sevilla, P. (2006). Unsupervised Image Segmentation Using a Hierarchical Clustering Selection Process. In: Yeung, DY., Kwok, J.T., Fred, A., Roli, F., de Ridder, D. (eds) Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2006. Lecture Notes in Computer Science, vol 4109. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11815921_88
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DOI: https://doi.org/10.1007/11815921_88
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