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ECCV
2004
Springer

Interactive Image Segmentation Using an Adaptive GMMRF Model

12 years 11 months ago
Interactive Image Segmentation Using an Adaptive GMMRF Model
The problem of interactive foreground/background segmentation in still images is of great practical importance in image editing. The state of the art in interactive segmentation is probably represented by the graph cut algorithm of Boykov and Jolly (ICCV 2001). Its underlying model uses both colour and contrast information, together with a strong prior for region coherence. Estimation is performed by solving a graph cut problem for which very efficient algorithms have recently been developed. However the model depends on parameters which must be set by hand and the aim of this work is for those constants to be learned from image data. First, a generative, probabilistic formulation of the model is set out in terms of a "Gaussian Mixture Markov Random Field" (GMMRF). Secondly, a pseudolikelihood algorithm is derived which jointly learns the colour mixture and coherence parameters for foreground and background respectively. Error rates for GMMRF segmentation are calculated throu...
Andrew Blake, Carsten Rother, M. Brown, Patrick P&
Added 15 Oct 2009
Updated 15 Oct 2009
Type Conference
Year 2004
Where ECCV
Authors Andrew Blake, Carsten Rother, M. Brown, Patrick Pérez, Philip H. S. Torr
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