Decision Tree Fields: An Efficient Non-parametric Random Field Model for Image Labeling

This chapter introduces a new random field model for discrete image labeling tasks, the Decision Tree Field (DTF), that combines and generalizes decision forests and conditional random fields (CRF) which have been widely used in computer vision.

[1]  Stephen Gould,et al.  Decomposing a scene into geometric and semantically consistent regions , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[2]  Zhuowen Tu,et al.  Auto-Context and Its Application to High-Level Vision Tasks and 3D Brain Image Segmentation , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Andrew W. Fitzgibbon,et al.  Real-time human pose recognition in parts from single depth images , 2011, CVPR 2011.

[4]  Sinisa Todorovic,et al.  (RF)^2 - Random Forest Random Field , 2010, NIPS.

[5]  Sebastian Nowozin,et al.  Decision tree fields , 2011, 2011 International Conference on Computer Vision.

[6]  Andrew W. Fitzgibbon,et al.  Learning Class-Specific Edges for Object Detection and Segmentation , 2006, ICVGIP.

[7]  Antonio Criminisi,et al.  TextonBoost for Image Understanding: Multi-Class Object Recognition and Segmentation by Jointly Modeling Texture, Layout, and Context , 2007, International Journal of Computer Vision.

[8]  Vladimir Kolmogorov,et al.  "GrabCut": interactive foreground extraction using iterated graph cuts , 2004, ACM Trans. Graph..

[9]  Andrew McCallum,et al.  An Introduction to Conditional Random Fields for Relational Learning , 2007 .

[10]  Sabine Glesner,et al.  Constructing Flexible Dynamic Belief Networks from First-Order Probalistic Knowledge Bases , 1995, ECSQARU.

[11]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  R. Zemel,et al.  Multiscale conditional random fields for image labeling , 2004, CVPR 2004.

[13]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[14]  Honglak Lee,et al.  Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations , 2009, ICML '09.

[15]  Ben Taskar,et al.  Discriminative learning of Markov random fields for segmentation of 3D scan data , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[16]  Yali Amit,et al.  Shape Quantization and Recognition with Randomized Trees , 1997, Neural Computation.

[17]  Richard Szeliski,et al.  A content-aware image prior , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[18]  Pierre Geurts,et al.  Extremely randomized trees , 2006, Machine Learning.

[19]  Bernt Schiele,et al.  Automatic discovery of meaningful object parts with latent CRFs , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[20]  Andrew McCallum,et al.  Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.

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

[22]  Andrew Zisserman,et al.  Advances in Neural Information Processing Systems (NIPS) , 2007 .

[23]  J. Besag.  Efficiency of pseudolikelihood estimation for simple Gaussian fields , 1977 .

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

[25]  Sebastian Nowozin,et al.  Structured Learning and Prediction in Computer Vision , 2011, Found. Trends Comput. Graph. Vis..

[26]  Michael J. Black,et al.  Steerable Random Fields , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[27]  Vladimir Kolmogorov,et al.  What metrics can be approximated by geo-cuts, or global optimization of length/area and flux , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[28]  Nir Friedman,et al.  Probabilistic Graphical Models - Principles and Techniques , 2009 .

[29]  Tsuhan Chen,et al.  Learning class-specific affinities for image labelling , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[30]  Ben Taskar,et al.  Learning structured prediction models: a large margin approach , 2005, ICML.

[31]  Michael I. Jordan,et al.  Graphical Models, Exponential Families, and Variational Inference , 2008, Found. Trends Mach. Learn..

[32]  Mark W. Schmidt,et al.  Accelerated training of conditional random fields with stochastic gradient methods , 2006, ICML.

[33]  Toby Sharp,et al.  Implementing Decision Trees and Forests on a GPU , 2008, ECCV.

[34]  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.

[35]  Roberto Cipolla,et al.  Semantic texton forests for image categorization and segmentation , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[36]  Sebastian Nowozin,et al.  Global connectivity potentials for random field models , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[37]  Stan Z. Li,et al.  Markov Random Field Modeling in Computer Vision , 1995, Computer Science Workbench.

[38]  Jorge Nocedal,et al.  Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization , 1997, TOMS.

[39]  Richard Szeliski,et al.  A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[40]  Derek Hoiem,et al.  Learning CRFs Using Graph Cuts , 2008, ECCV.