Learning Full Pairwise Affinities
for Spectral Segmentation



This paper studies the problem of learning a full range of pairwise affinities gained by integrating local grouping cues for spectral segmentation. The overall quality of the spectral segmentation depends mainly on the pairwise pixel affinities. By employing a semi-supervised learning technique, optimal affinities are learnt from the test image without iteration. We first construct a multi-layer graph with pixels and regions, generated by the mean shift algorithm, as nodes. By applying the semi-supervised learning strategy to this graph, we can estimate the intra- and inter-layer affinities between all pairs of nodes together. These pairwise affinities are then used to simultaneously cluster all pixel and region nodes into visually coherent groups across all layers in a single multi-layer framework of Normalized Cuts. Our algorithm provides high-quality segmentations with object details by directly incorporating the full range connections in the spectral framework. Since the full affinity matrix is defined by the inverse of a sparse matrix, its eigen-decomposition is efficiently computed. The experimental results on Berkeley and MSRC image databases demonstrate the relevance and accuracy of our algorithm as compared to existing popular methods.


paper thumbnail


CVPR 2010 paper. (pdf, 1.78MB), [Supplement]. (pdf, 9.71MB)


Tae Hoon Kim, Kyoung Mu Lee, Sang Uk Lee. Learning Full Pairwise Affinities for Spectral Segmentation, Proc. Computer Vision and Pattern Recognition (CVPR), 2010

(ORAL presentation, 4.5% acceptance rate)


code. (zip, 1.30MB)


This research is supported in part by: