Learning Intrinsic Video Content Using Levenshtein Distance in Graph Partitioning
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Graph matching using spectral seriation and string edit distance
GbRPR'03 Proceedings of the 4th IAPR international conference on Graph based representations in pattern recognition
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ABSTRACT: This paper presents an iterative maximum likelihood framework for motion segmentation. Our representation of the segmentation problem is based on a similarity matrix for the motion vectors for pairs of pixel blocks. By applying eigen decomposition to the similarity matrix, we develop a maximum likelihood method for grouping the pixel blocks into objects which share a common motion vector. We experiment with the resulting clustering method on a number of real world motion sequences. Here ground truth data indicates that the method can result in motion classification errors as low as 3%.