Abstract
Pairwise data clustering techniques are gaining increasing popularity over traditional, feature-based central grouping techniques. These approaches have proved very powerful when applied to image-segmentation problems. However, they are mainly focused on extracting flat partitions of the data, thus missing out on the advantages of the inclusion constraints typical of hierarchical coarse-to-fine segmentations approaches very common when working directly on the image lattice. In this paper we present a pairwise hierarchical segmentation approach based on dominant sets [12] where an anisotropic diffusion kernel allows for a scale variation for the extraction of the segments, thus enforcing separations on strong boundaries at a high level of the hierarchy. Experimental results on the standard Berkeley database [9] show the effectiveness of the approach.
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Torsello, A., Pelillo, M. (2009). Hierarchical Pairwise Segmentation Using Dominant Sets and Anisotropic Diffusion Kernels. In: Cremers, D., Boykov, Y., Blake, A., Schmidt, F.R. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2009. Lecture Notes in Computer Science, vol 5681. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03641-5_14
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DOI: https://doi.org/10.1007/978-3-642-03641-5_14
Publisher Name: Springer, Berlin, Heidelberg
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