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.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Chuang, Y.Y., Curless, B., Salesin, D., Szeliski, R.: A Bayesian approach to digital matting. In: Proc. Conf. Computer Vision and Pattern Recognition, CD–ROM (2001)
Ruzon, M., Tomasi, C.: Alpha estimation in natural images. In: Proc. Conf. ComputerVision and Pattern Recognition, pp. 18–25 (2000)
Boykov, Y., Jolly, M.P.: Interactive graph cuts for optimal boundary and region segmentation of objects in N-D images. In: Proc. Int. Conf. on Computer Vision, CD–ROM (2001)
Greig, D., Porteous, B., Seheult, A.: Exact MAP estimation for binary images. J. Royal Statistical Society 51, 271–279 (1989)
Kolmogorov, V., Zabih, R.: What energy functions can be minimized via graph cuts? IEEE Trans. on Pattern Analysis and Machine Intelligence (2003) (in press)
Besag, J.: On the statistical analysis of dirty pictures. J. Roy. Stat. Soc. Lond. B. 48, 259–302 (1986)
Winkler, G.: Image analysis, random fields and dynamic Monte Carlo methods. Springer, Heidelberg (1995)
Kumar, S., Hebert, M.: Discriminative random fields:A discriminative framework for contextual interaction in classification. In: Proc. Int. Conf. on Computer Vision, CD–ROM (2003)
Descombes, X., Sigelle, M., Preteux, F.: GMRF parameter estimation in a non-stationary framework by a renormalization technique. IEEE Trans. Image Processing 8, 490–503 (1999)
Malik, J., Belongie, S., Leung, T., Shi, J.: Contour and texture analysis for image segmentation. Int. J. Computer Vision 43, 7–27 (2001)
Geman, S., Geman, D.: Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans. on Pattern Analysis and Machine Intelligence 6, 721–741 (1984)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Blake, A., Rother, C., Brown, M., Perez, P., Torr, P. (2004). Interactive Image Segmentation Using an Adaptive GMMRF Model. In: Pajdla, T., Matas, J. (eds) Computer Vision - ECCV 2004. ECCV 2004. Lecture Notes in Computer Science, vol 3021. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24670-1_33
Download citation
DOI: https://doi.org/10.1007/978-3-540-24670-1_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-21984-2
Online ISBN: 978-3-540-24670-1
eBook Packages: Springer Book Archive