Graph based image segmentation

Image segmentation refers to a process of dividing the image into disjoint regions that were meaningful. This process is fundamental in computer vision in that many applications, such as image retrieval, visual summary, image based modeling, and so on, can essentially benefit from it. This process is also challenging because the segmentation is usually subjective and the computation is highly costly. This book develops in turn the prior model for the pairwise graph approaches which is defined from multiple cues, a hyper graph based method which models multiple wise relations among the data points, and a tree structured graph based method which leads to an efficient and effective solution to the normalized cuts criterion. These approaches are demonstrated in multiple view, interactive and automatic image segmentation problems. This book is suitable for students and researchers in image processing, computer vision, pattern recognition and machine learning.

[1]  M. Wertheimer Laws of organization in perceptual forms. , 1938 .

[2]  C. N. Liu,et al.  Approximating discrete probability distributions with dependence trees , 1968, IEEE Trans. Inf. Theory.

[3]  J. Cheeger A lower bound for the smallest eigenvalue of the Laplacian , 1969 .

[4]  Claude L. Fennema,et al.  Scene Analysis Using Regions , 1970, Artif. Intell..

[5]  Ugo Montanari,et al.  On the optimal detection of curves in noisy pictures , 1971, CACM.

[6]  Charles T. Zahn,et al.  Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters , 1971, IEEE Transactions on Computers.

[7]  A. Hoffman,et al.  Lower bounds for the partitioning of graphs , 1973 .

[8]  Alex Pothen,et al.  PARTITIONING SPARSE MATRICES WITH EIGENVECTORS OF GRAPHS* , 1990 .

[9]  M. Fiedler A property of eigenvectors of nonnegative symmetric matrices and its application to graph theory , 1975 .

[10]  Theodosios Pavlidis,et al.  Picture Segmentation by a Tree Traversal Algorithm , 1976, JACM.

[11]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[12]  T. Pavlidis,et al.  A graph-theoretic approach to picture processing , 1978 .

[13]  N. Otsu A threshold selection method from gray level histograms , 1979 .

[14]  Roderick Urquhart,et al.  Graph theoretical clustering based on limited neighbourhood sets , 1982, Pattern Recognit..

[15]  H. Barlow Vision: A computational investigation into the human representation and processing of visual information: David Marr. San Francisco: W. H. Freeman, 1982. pp. xvi + 397 , 1983 .

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

[17]  N. Alon Eigenvalues and expanders , 1986, Comb..

[18]  J. Besag On the Statistical Analysis of Dirty Pictures , 1986 .

[19]  Ravi B. Boppana,et al.  Eigenvalues and graph bisection: An average-case analysis , 1987, 28th Annual Symposium on Foundations of Computer Science (sfcs 1987).

[20]  Mark Jerrum,et al.  Approximate Counting, Uniform Generation and Rapidly Mixing Markov Chains , 1987, WG.

[21]  Wang,et al.  Nonuniversal critical dynamics in Monte Carlo simulations. , 1987, Physical review letters.

[22]  Olivier Monga,et al.  An Optimal Region Growing Algorithm for Image Segmentation , 1987, Int. J. Pattern Recognit. Artif. Intell..

[23]  W. Wong,et al.  The calculation of posterior distributions by data augmentation , 1987 .

[24]  Shimon Ullman,et al.  Structural Saliency: The Detection Of Globally Salient Structures using A Locally Connected Network , 1988, [1988 Proceedings] Second International Conference on Computer Vision.

[25]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[26]  Steven W. Zucker,et al.  Trace Inference, Curvature Consistency, and Curve Detection , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[27]  D. Greig,et al.  Exact Maximum A Posteriori Estimation for Binary Images , 1989 .

[28]  Luc Vincent,et al.  Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[29]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[30]  Andrew B. Kahng,et al.  A new approach to effective circuit clustering , 1992, ICCAD.

[31]  Richard M. Leahy,et al.  An Optimal Graph Theoretic Approach to Data Clustering: Theory and Its Application to Image Segmentation , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[32]  Edward H. Adelson,et al.  Representing moving images with layers , 1994, IEEE Trans. Image Process..

[33]  Andy Harter,et al.  Parameterisation of a stochastic model for human face identification , 1994, Proceedings of 1994 IEEE Workshop on Applications of Computer Vision.

[34]  M.C. Clark,et al.  MRI segmentation using fuzzy clustering techniques , 1994, IEEE Engineering in Medicine and Biology Magazine.

[35]  Martine D. F. Schlag,et al.  Spectral K-way ratio-cut partitioning and clustering , 1994, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst..

[36]  Rolf Adams,et al.  Seeded Region Growing , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[37]  Ruzena Bajcsy,et al.  Segmentation Modeling , 1995, CAIP.

[38]  Shang-Hua Teng,et al.  Disk packings and planar separators , 1996, SCG '96.

[39]  Alan L. Yuille,et al.  Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[40]  Sameer A. Nene,et al.  Columbia Object Image Library (COIL100) , 1996 .

[41]  Lance R. Williams,et al.  Stochastic Completion Fields: A Neural Model of Illusory Contour Shape and Salience , 1997, Neural Computation.

[42]  Brendan J. Frey,et al.  A Revolution: Belief Propagation in Graphs with Cycles , 1997, NIPS.

[43]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[44]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[45]  Arthur Robert Weeks,et al.  Color segmentation in the HSI color space using the K-means algorithm , 1997, Electronic Imaging.

[46]  Ingemar J. Cox,et al.  A maximum-flow formulation of the N-camera stereo correspondence problem , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[47]  Avrim Blum,et al.  The Bottleneck , 2021, Monopsony Capitalism.

[48]  Davi Geiger,et al.  Segmentation by grouping junctions , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[49]  Maxime Lhuillier,et al.  Efficient Dense Matching for Textured Scenes using Region Growing , 1998, BMVC.

[50]  Vipin Kumar,et al.  A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs , 1998, SIAM J. Sci. Comput..

[51]  Olga Veksler,et al.  Fast approximate energy minimization via graph cuts , 2001, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[52]  Richard Szeliski,et al.  An integrated Bayesian approach to layer extraction from image sequences , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[53]  Thorsten Joachims,et al.  Transductive Inference for Text Classification using Support Vector Machines , 1999, ICML.

[54]  James A. Sethian,et al.  Level Set Methods and Fast Marching Methods , 1999 .

[55]  Josef Kittler,et al.  Probabilistic PCA and ICA Subspace Mixture Models for Image Segmentation , 2000, BMVC.

[56]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[57]  W. Freeman,et al.  Generalized Belief Propagation , 2000, NIPS.

[58]  Avrim Blum,et al.  Learning from Labeled and Unlabeled Data using Graph Mincuts , 2001, ICML.

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

[60]  Michael I. Jordan,et al.  Mixtures of Probabilistic Principal Component Analyzers , 2001 .

[61]  Philip N. Klein,et al.  Recognition of Shapes by Editing Shock Graphs , 2001, ICCV.

[62]  Ioannis Patras,et al.  Video Segmentation by MAP Labeling of Watershed Segments , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[63]  Tom M. Mitchell,et al.  Using unlabeled data to improve text classification , 2001 .

[64]  Harry Shum,et al.  Image segmentation by data driven Markov chain Monte Carlo , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[65]  Chris H. Q. Ding,et al.  A min-max cut algorithm for graph partitioning and data clustering , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[66]  Mikhail Belkin,et al.  Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering , 2001, NIPS.

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

[68]  Zoubin Ghahramani,et al.  Learning from labeled and unlabeled data with label propagation , 2002 .

[69]  Vladimir Kolmogorov,et al.  Multi-camera Scene Reconstruction via Graph Cuts , 2002, ECCV.

[70]  Shimon Ullman,et al.  Class-Specific, Top-Down Segmentation , 2002, ECCV.

[71]  Bernhard Schölkopf,et al.  Cluster Kernels for Semi-Supervised Learning , 2002, NIPS.

[72]  Ming-Hsuan Yang,et al.  Kernel Eigenfaces vs. Kernel Fisherfaces: Face recognition using kernel methods , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[73]  Jitendra Malik,et al.  Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[74]  Zoubin Ghahramani,et al.  Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions , 2003, ICML 2003.

[75]  Tomer Hertz,et al.  Learning and inferring image segmentations using the GBP typical cut algorithm , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[76]  Bernhard Schölkopf,et al.  Ranking on Data Manifolds , 2003, NIPS.

[77]  Bernhard Schölkopf,et al.  Learning with Local and Global Consistency , 2003, NIPS.

[78]  Thorsten Joachims,et al.  Transductive Learning via Spectral Graph Partitioning , 2003, ICML.

[79]  Irfan A. Essa,et al.  Graphcut textures: image and video synthesis using graph cuts , 2003, ACM Trans. Graph..

[80]  Anthony Widjaja,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2003, IEEE Transactions on Neural Networks.

[81]  Jianbo Shi,et al.  Multiclass spectral clustering , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[82]  Jeffrey Mark Siskind,et al.  Image Segmentation with Ratio Cut , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[83]  Serge J. Belongie,et al.  What went where , 2003, CVPR 2003.

[84]  Jianbo Shi,et al.  Object-Specific Figure-Ground Segregation , 2003, CVPR.

[85]  Vladimir Kolmogorov,et al.  Spatially coherent clustering using graph cuts , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[86]  Shimon Ullman,et al.  Combining Top-Down and Bottom-Up Segmentation , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[87]  Jitendra Malik,et al.  Contour and Texture Analysis for Image Segmentation , 2001, International Journal of Computer Vision.

[88]  Harry Shum,et al.  Lazy snapping , 2004, ACM Trans. Graph..

[89]  Mubarak Shah,et al.  Motion layer extraction in the presence of occlusion using graph cuts , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[90]  Michael I. Jordan,et al.  An Introduction to Variational Methods for Graphical Models , 1999, Machine Learning.

[91]  Nando de Freitas,et al.  From Fields to Trees , 2004, UAI.

[92]  Guillermo Sapiro,et al.  Geodesic Active Contours , 1995, International Journal of Computer Vision.

[93]  William T. Freeman,et al.  Learning Low-Level Vision , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[94]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[95]  Daniel P. Huttenlocher,et al.  Efficient Belief Propagation for Early Vision , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[96]  Mikhail Belkin,et al.  Regularization and Semi-supervised Learning on Large Graphs , 2004, COLT.

[97]  Shimon Ullman,et al.  Learning to Segment , 2004, ECCV.

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

[99]  Bernhard Schölkopf,et al.  Learning from Labeled and Unlabeled Data Using Random Walks , 2004, DAGM-Symposium.

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

[101]  John D. Lafferty,et al.  Semi-supervised learning using randomized mincuts , 2004, ICML.

[102]  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).

[103]  Olga Veksler,et al.  Stereo correspondence by dynamic programming on a tree , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[104]  Naonori Ueda,et al.  A Hybrid Generative/Discriminative Approach to Semi-Supervised Classifier Design , 2005, AAAI.

[105]  Harry Shum,et al.  Modeling hair from multiple views , 2005, ACM Trans. Graph..

[106]  Changshui Zhang,et al.  Spectral feature analysis , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

[107]  Zhuowen Tu,et al.  Image Parsing: Unifying Segmentation, Detection, and Recognition , 2005, International Journal of Computer Vision.

[108]  Martial Hebert,et al.  Semi-Supervised Self-Training of Object Detection Models , 2005, 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05) - Volume 1.

[109]  Andrew Blake,et al.  Bi-layer segmentation of binocular stereo video , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[110]  Adrian Barbu,et al.  Generalizing Swendsen-Wang to sampling arbitrary posterior probabilities , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[111]  Nicolas Le Roux,et al.  Efficient Non-Parametric Function Induction in Semi-Supervised Learning , 2004, AISTATS.

[112]  Xiaojin Zhu,et al.  Harmonic mixtures: combining mixture models and graph-based methods for inductive and scalable semi-supervised learning , 2005, ICML.

[113]  Brendan J. Frey,et al.  A comparison of algorithms for inference and learning in probabilistic graphical models , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[114]  Long Quan,et al.  A quasi-dense approach to surface reconstruction from uncalibrated images , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[115]  Xiaojin Zhu,et al.  --1 CONTENTS , 2006 .

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

[117]  Tony F. Chan,et al.  Level set based shape prior segmentation , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[118]  Leo Grady,et al.  Multilabel random walker image segmentation using prior models , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[119]  Leonhard Held,et al.  Gaussian Markov Random Fields: Theory and Applications , 2005 .

[120]  Long Quan,et al.  Image-based plant modeling , 2006, ACM Trans. Graph..

[121]  Harry Shum,et al.  Background Cut , 2006, ECCV.

[122]  Leo Grady,et al.  Random Walks for Image Segmentation , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[123]  A. Criminisi,et al.  Bilayer Segmentation of Live Video , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[124]  Serge J. Belongie,et al.  Higher order learning with graphs , 2006, ICML.

[125]  Bernhard Schölkopf,et al.  Learning with Hypergraphs: Clustering, Classification, and Embedding , 2006, NIPS.

[126]  Olivier Juan,et al.  Active Graph Cuts , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[127]  Gareth Funka-Lea,et al.  Graph Cuts and Efficient N-D Image Segmentation , 2006, International Journal of Computer Vision.

[128]  Helen C. Shen,et al.  Semi-Supervised Classification Using Linear Neighborhood Propagation , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

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

[130]  L. Rosasco,et al.  Manifold Regularization , 2007 .

[131]  Long Quan,et al.  Image-based tree modeling , 2007, ACM Trans. Graph..

[132]  Fei Wang,et al.  Face recognition using spectral features , 2007, Pattern Recognit..

[133]  Long Quan,et al.  Accurate and Scalable Surface Representation and Reconstruction from Images , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[134]  Jianxiong Xiao,et al.  Joint Affinity Propagation for Multiple View Segmentation , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[135]  Long Quan,et al.  Image-Based Modeling by Joint Segmentation , 2007, International Journal of Computer Vision.

[136]  Dani Lischinski,et al.  A Closed-Form Solution to Natural Image Matting , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[137]  Anat Levin,et al.  Learning to Combine Bottom-Up and Top-Down Segmentation , 2006, International Journal of Computer Vision.

[138]  Helen C. Shen,et al.  Linear Neighborhood Propagation and Its Applications , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[139]  Harry Shum,et al.  Picture Collage , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).