Unsupervised Image Segmentation Using a Hierarchical Clustering Selection Process
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.
KeywordsImage Segmentation Segmentation Result Active Contour Criterion Function Multispectral Image
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- 2.Buhmann, J.: Data clustering and learning, The Handbook of Brain Theory and Neural Networks, 2nd edn., pp. 308–312 (2002)Google Scholar
- 4.Gurari, E.M., Wechsler, H.: On the difficulties involved in the segmentation of pictures. PAMI(4) (3), 304–306 (1982)Google Scholar
- 8.Muñoz, X., Freixenet, J., Cufí, X., Martí, J.: Strategies for image segmentation combining region and boundary information. PRL 24(1-3), 375–392 (2003)Google Scholar
- 9.Nixon, M., Aguado, A.S.: Feature Extraction in Computer Vision and Image Processing (2002)Google Scholar
- 12.Samet, H.: Applications of Spatial Data Structures: Computer Graphics, Image Processing and GIS (1990)Google Scholar
- 13.Zhang, Y.J.: A review of recent evaluation methods for image segmentation. In: Proceedings of the Sixth ISSPA, vol. 1, pp. 148–151 (2001)Google Scholar