Trimap Segmentation for Fast and User-Friendly Alpha Matting

Given an image, digital matting consists in extracting a foreground element from the background. Standard methods are initialized with a trimap, a partition of the image into three regions: a definite foreground, a definite background, and a blended region where pixels are considered as a mixture of foreground and background colors. Recovering these colors and the proportion of mixture between both is an under-constrained inverse problem, sensitive to its initialization: one has to specify an accurate trimap, leaving undetermined as few pixels as possible. First, we propose a new segmentation scheme to extract an accurate trimap from just a coarse indication of some background and/or foreground pixels. Standard statistical models are used for the foreground and the background, while a specific one is designed for the blended region. The segmentation of the three regions is conducted simultaneously by an iterative Graph Cut based optimization scheme. This user-friendly trimap is similar to carefully hand specified ones. As a second step, we take advantage of our blended region model to design an improved matting method coherent. Based on global statistics rather than on local ones, our method is much faster than standard Bayesian matting, without quality loss, and also usable with manual trimaps.

[1]  Rachid Deriche,et al.  Active unsupervised texture segmentation on a diffusion based feature space , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[2]  Geoffrey J. McLachlan,et al.  Finite Mixture Models , 2019, Annual Review of Statistics and Its Application.

[3]  Hiroshi Ishikawa,et al.  Exact Optimization for Markov Random Fields with Convex Priors , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Michael T. Orchard,et al.  Color quantization of images , 1991, IEEE Trans. Signal Process..

[5]  Marie-Pierre Jolly,et al.  Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[6]  G. McLachlan,et al.  The EM algorithm and extensions , 1996 .

[7]  R. Casey,et al.  Advances in Pattern Recognition , 1971 .

[8]  Renaud Keriven,et al.  Stochastic Motion and the Level Set Method in Computer Vision: Stochastic Active Contours , 2006, International Journal of Computer Vision.

[9]  Patrick Pérez,et al.  Interactive Image Segmentation Using an Adaptive GMMRF Model , 2004, ECCV.

[10]  Rachid Deriche,et al.  Unsupervised Segmentation Incorporating Colour, Texture, and Motion , 2003, CAIP.

[11]  Takashi Totsuka,et al.  AutoKey: human assisted key extraction , 1995, SIGGRAPH.

[12]  Andrew W. Fitzgibbon,et al.  Bayesian Estimation of Layers from Multiple Images , 2002, ECCV.

[13]  Jiří Matas,et al.  Computer Vision - ECCV 2004 , 2004, Lecture Notes in Computer Science.

[14]  Vladimir Kolmogorov,et al.  Computing geodesics and minimal surfaces via graph cuts , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[15]  S. Osher,et al.  Geometric Level Set Methods in Imaging, Vision, and Graphics , 2011, Springer New York.

[16]  Geoffrey E. Hinton,et al.  SMEM Algorithm for Mixture Models , 1998, Neural Computation.

[17]  Patrick Pérez,et al.  Poisson image editing , 2003, ACM Trans. Graph..

[18]  Mubarak Shah,et al.  Accurate motion layer segmentation and matting , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[19]  S. Osher,et al.  Algorithms Based on Hamilton-Jacobi Formulations , 1988 .

[20]  Vladimir Kolmogorov,et al.  What energy functions can be minimized via graph cuts? , 2002, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Carlo Tomasi,et al.  Alpha estimation in natural images , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[22]  H. L. Le Roy,et al.  Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability; Vol. IV , 1969 .

[23]  Michael Brady,et al.  Unsupervised non-parametric region segmentation using level sets , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[24]  Mads Nielsen,et al.  Computer Vision — ECCV 2002 , 2002, Lecture Notes in Computer Science.

[25]  Jiaya Jia,et al.  Poisson matting , 2004, SIGGRAPH 2004.

[26]  James F. Blinn,et al.  Blue screen matting , 1996, SIGGRAPH.

[27]  Xavier Descombes,et al.  Estimating Gaussian Markov random field parameters in a nonstationary framework: application to remote sensing imaging , 1999, IEEE Trans. Image Process..

[28]  Mikio Takagi,et al.  The area proportion distribution - relationship with the internal structure of mixels and its application to image classification , 2000, Syst. Comput. Jpn..

[29]  Asanobu Kitamoto.  The Moments of the Mixel Distribution and Its Application to Statistical Image Classification , 2000, SSPR/SPR.

[30]  Olga Veksler,et al.  Fast Approximate Energy Minimization via Graph Cuts , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[31]  J. Sethian,et al.  Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations , 1988 .

[32]  David Salesin,et al.  A Bayesian approach to digital matting , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[33]  Marie-Pierre Jolly,et al.  Interactive Graph Cuts for Optimal Boundary and Region Segmentation of Objects in N-D Images , 2001, ICCV.

[34]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[35]  Andrew Blake,et al.  "GrabCut" , 2004, ACM Trans. Graph..

[36]  José M. N. Leitão,et al.  On Fitting Mixture Models , 1999, EMMCVPR.