Interactive image segmentation using an adaptive GMMRF model

Interactive image segmentation using an adaptive GMMRF model (2004)

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by A. Blake , C. Rother , M. Brown , P. Perez , P. Torr

Venue:	in ECCV
Citations:	102 - 20 self

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Datum	Value	Source
TITLE	Interactive image segmentation using an adaptive GMMRF model	INFERENCE
AUTHOR NAME	A. Blake	SVM HeaderParse 0.2
AUTHOR AFFIL	Microsoft Research Cambridge UK,; 7 JJ Thomson Avenue, Cambridge CB3 0FB	SVM HeaderParse 0.2
AUTHOR ADDR	UK.	SVM HeaderParse 0.2
AUTHOR NAME	C. Rother	SVM HeaderParse 0.2
AUTHOR AFFIL	Microsoft Research Cambridge UK,; 7 JJ Thomson Avenue, Cambridge CB3 0FB	SVM HeaderParse 0.2
AUTHOR ADDR	UK.	SVM HeaderParse 0.2
AUTHOR NAME	M. Brown	SVM HeaderParse 0.2
AUTHOR AFFIL	Microsoft Research Cambridge UK,; 7 JJ Thomson Avenue, Cambridge CB3 0FB	SVM HeaderParse 0.2
AUTHOR ADDR	UK.	SVM HeaderParse 0.2
AUTHOR NAME	P. Perez	SVM HeaderParse 0.2
AUTHOR AFFIL	Microsoft Research Cambridge UK,; 7 JJ Thomson Avenue, Cambridge CB3 0FB	SVM HeaderParse 0.2
AUTHOR ADDR	UK.	SVM HeaderParse 0.2
AUTHOR NAME	P. Torr	SVM HeaderParse 0.2
AUTHOR AFFIL	Microsoft Research Cambridge UK,; 7 JJ Thomson Avenue, Cambridge CB3 0FB	SVM HeaderParse 0.2
AUTHOR ADDR	UK.	SVM HeaderParse 0.2
ABSTRACT	Abstract. 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 throughout using a new image database, available on the web, with ground truth provided by a human segmenter. The graph cut algorithm, using the learned parameters, generates good object-segmentations with little interaction. However, pseudolikelihood learning proves to be frail, which limits the complexity of usable models, and hence also the achievable error rate. 1	SVM HeaderParse 0.2
YEAR	2004	INFERENCE
VENUE	in ECCV	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	428--441	INFERENCE
VOLUME	1	INFERENCE
CITATIONS	11 found	ParsCit 1.0