Quantitative measures of change based on feature organization: eigenvalues and eigenvectors
Computer Vision and Image Understanding
Spectral compression of mesh geometry
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour and Texture Analysis for Image Segmentation
International Journal of Computer Vision
Pairwise Data Clustering by Deterministic Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Diffusion Kernels on Graphs and Other Discrete Input Spaces
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Path-Based Clustering for Grouping of Smooth Curves and Texture Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dominant Sets and Hierarchical Clustering
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Optimal Cluster Preserving Embedding of Nonmetric Proximity Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spectral Grouping Using the Nyström Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Random Walks for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dominant Sets and Pairwise Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Integrating Boundary Information in Pairwise Segmentation
ICIAP '07 Proceedings of the 14th International Conference on Image Analysis and Processing
Graph spectral image smoothing
GbRPR'07 Proceedings of the 6th IAPR-TC-15 international conference on Graph-based representations in pattern recognition
Spatio-temporal segmentation using dominant sets
EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
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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.