Graph cut with ordering constraints on labels and its applications

In the last decade, graph-cut optimization has been popular for a variety of pixel labeling problems. Typically graph-cut methods are used to incorporate a smoothness prior on a labeling. Recently several methods incorporated ordering constraints on labels for the application of object segmentation. An example of an ordering constraint is prohibiting a pixel with a ldquocar wheelrdquo label to be above a pixel with a ldquocar roofrdquo label. We observe that the commonly used graph-cut based alpha-expansion is more likely to get stuck in a local minimum when ordering constraints are used. For certain models with ordering constraints, we develop new graph-cut moves which we call order-preserving moves. Order-preserving moves act on all labels, unlike alpha-expansion. Although the global minimum is still not guaranteed, optimization with order-preserving moves performs significantly better than alpha-expansion. We evaluate order-preserving moves for the geometric class scene labeling (introduced by Hoiem et al.) where the goal is to assign each pixel a label such as ldquoskyrdquo, ldquogrounrdquo, etc., so ordering constraints arise naturally. In addition, we use order-preserving moves for certain simple shape priors in graphcut segmentation, which is a novel contribution in itself.

[1]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[2]  Alexei A. Efros,et al.  Recovering Surface Layout from an Image , 2007, International Journal of Computer Vision.

[3]  Daniel Cremers,et al.  Kernel Density Estimation and Intrinsic Alignment for Shape Priors in Level Set Segmentation , 2006, International Journal of Computer Vision.

[4]  Jérôme Darbon,et al.  Global optimization for first order Markov Random Fields with submodular priors , 2008, Discret. Appl. Math..

[5]  Jens Keuchel Multiclass Image Labeling with Semidefinite Programming , 2006, ECCV.

[6]  Daniel P. Huttenlocher,et al.  Efficient Graph-Based Image Segmentation , 2004, International Journal of Computer Vision.

[7]  O. Faugeras Three-dimensional computer vision: a geometric viewpoint , 1993 .

[8]  Ian D. Reid,et al.  Single View Metrology , 2000, International Journal of Computer Vision.

[9]  Alexei A. Efros,et al.  Putting Objects in Perspective , 2006, CVPR.

[10]  Jitendra Malik,et al.  Modeling and Rendering Architecture from Photographs: A hybrid geometry- and image-based approach , 1996, SIGGRAPH.

[11]  Ashutosh Saxena,et al.  3-D Depth Reconstruction from a Single Still Image , 2007, International Journal of Computer Vision.

[12]  Vladimir Kolmogorov,et al.  Convergent Tree-Reweighted Message Passing for Energy Minimization , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Leo Grady,et al.  Uninitialized, globally optimal, graph-based rectilinear shape segmentation - the opposing metrics method , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[14]  Derek Hoiem,et al.  3D LayoutCRF for Multi-View Object Class Recognition and Segmentation , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  Alexei A. Efros,et al.  Geometric context from a single image , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[16]  Tao Zhang,et al.  Interactive graph cut based segmentation with shape priors , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[17]  Nikos Paragios,et al.  Shape Priors for Level Set Representations , 2002, ECCV.

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

[19]  Mei Han,et al.  Interactive construction of 3D models from panoramic mosaics , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[20]  W. Freeman,et al.  Bethe free energy, Kikuchi approximations, and belief propagation algorithms , 2001 .

[21]  Honglak Lee,et al.  A Dynamic Bayesian Network Model for Autonomous 3D Reconstruction from a Single Indoor Image , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[22]  Ken-ichi Anjyo,et al.  Tour into the picture: using a spidery mesh interface to make animation from a single image , 1997, SIGGRAPH.

[23]  Andrew Zisserman,et al.  OBJ CUT , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[24]  John Platt,et al.  Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods , 1999 .

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

[26]  D. Schlesinger,et al.  TRANSFORMING AN ARBITRARY MINSUM PROBLEM INTO A BINARY ONE , 2006 .

[27]  Vladimir Kolmogorov,et al.  An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision , 2004, IEEE Trans. Pattern Anal. Mach. Intell..

[28]  Jamie Shotton,et al.  The Layout Consistent Random Field for Recognizing and Segmenting Partially Occluded Objects , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[29]  Alan L. Yuille,et al.  Manhattan World: compass direction from a single image by Bayesian inference , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[30]  Alexei A. Efros,et al.  Automatic photo pop-up , 2005, SIGGRAPH 2005.

[31]  Richard Szeliski,et al.  A Comparative Study of Energy Minimization Methods for Markov Random Fields , 2006, ECCV.

[32]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[33]  Olivier D. Faugeras,et al.  Statistical shape influence in geodesic active contours , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).