Learning Full Pairwise Affinities for Spectral Segmentation
published: Dec. 27, 2010, recorded: June 2010, views: 174
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
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 eigendecomposition 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.
Download slides: cvpr2010_hoon_kim_lfpa_01.v1.pdf (2.3 MB)
Download article: cvpr2010_hoon_kim_lfpa_01.pdf (1.8 MB)
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !