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.

Hierarchical Pairwise Segmentation using Dominant Sets and Anisotropic Diffusion Kernels

TORSELLO, Andrea;PELILLO, Marcello
2009-01-01

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.
2009
Energy Minimization Methods in Computer Vision and Pattern Recognition
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/22544
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