Natural Image Statistics for Natural Image Segmentation

Building on recent progress in modeling filter response statistics of natural images we integrate a statistical model into a variational framework for image segmentation. Incorporated in a sound probabilistic distance measure the model drives level sets toward meaningful segmentations of complex textures and natural scenes. Since each region comprises two model parameters only the approach is computationally efficient and enables the application of variational segmentation to a considerably larger class of real-world images. We validate the statistical basis of our approach on thousands of natural images and demonstrate that our model outperforms recent variational segmentation methods based on second-order statistics.

[1]  Thomas R. Fischer,et al.  Image subband coding using arithmetic coded trellis coded quantization , 1995, IEEE Trans. Circuits Syst. Video Technol..

[2]  Jitendra Malik,et al.  Color- and texture-based image segmentation using EM and its application to content-based image retrieval , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[3]  Bin Yu,et al.  Model Selection and the Principle of Minimum Description Length , 2001 .

[4]  Eero P. Simoncelli.  Statistical models for images: compression, restoration and synthesis , 1997, Conference Record of the Thirty-First Asilomar Conference on Signals, Systems and Computers (Cat. No.97CB36136).

[5]  Song-Chun Zhu,et al.  Prior Learning and Gibbs Reaction-Diffusion , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Emanuele Trucco,et al.  Robust motion and correspondence of noisy 3-D point sets with missing data , 1999, Pattern Recognit. Lett..

[7]  Yvan G. Leclerc,et al.  Constructing simple stable descriptions for image partitioning , 1989, International Journal of Computer Vision.

[8]  Jerry D. Gibson,et al.  Distributions of the Two-Dimensional DCT Coefficients for Images , 1983, IEEE Trans. Commun..

[9]  Ming Tang,et al.  General Scheme of Region Competition Based on Scale Space , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Daniel Cremers,et al.  Multiphase Dynamic Labeling for Variational Recognition-Driven Image Segmentation , 2004, ECCV.

[11]  Arnold W. M. Smeulders,et al.  Fragmentation in the vision of scenes , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[12]  Michael T. Orchard,et al.  Image coding based on mixture modeling of wavelet coefficients and a fast estimation-quantization framework , 1997, Proceedings DCC '97. Data Compression Conference.

[13]  Leonidas J. Guibas,et al.  The Earth Mover's Distance as a Metric for Image Retrieval , 2000, International Journal of Computer Vision.

[14]  William T. Freeman,et al.  Presented at: 2nd Annual IEEE International Conference on Image , 1995 .

[15]  J. Rissanen,et al.  Modeling By Shortest Data Description* , 1978, Autom..

[16]  Anthony J. Yezzi,et al.  Gradient flows and geometric active contour models , 1995, Proceedings of IEEE International Conference on Computer Vision.

[17]  DeLiang Wang,et al.  Texture classification using spectral histograms , 2003, IEEE Trans. Image Process..

[18]  Zhuowen Tu,et al.  Image Segmentation by Data-Driven Markov Chain Monte Carlo , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  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..

[20]  Joachim M. Buhmann,et al.  Histogram clustering for unsupervised segmentation and image retrieval , 1999, Pattern Recognit. Lett..

[21]  D. Mumford,et al.  Optimal approximations by piecewise smooth functions and associated variational problems , 1989 .

[22]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  Edward H. Adelson,et al.  The Design and Use of Steerable Filters , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

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

[25]  Michel Barlaud,et al.  DREAM2S: Deformable Regions Driven by an Eulerian Accurate Minimization Method for Image and Video Segmentation , 2002, ECCV.

[26]  Daniel Cremers,et al.  Binary Partitioning, Perceptual Grouping, and Restoration with Semidefinite Programming , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[27]  C. S. Wallace,et al.  An Information Measure for Classification , 1968, Comput. J..

[28]  Edward H. Adelson,et al.  Noise removal via Bayesian wavelet coring , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[29]  Song-Chun Zhu,et al.  Filters, Random Fields and Maximum Entropy (FRAME): Towards a Unified Theory for Texture Modeling , 1998, International Journal of Computer Vision.

[30]  Rachid Deriche,et al.  Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation , 2002, International Journal of Computer Vision.

[31]  Trygve Randen,et al.  Filtering for Texture Classification: A Comparative Study , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[32]  Anuj Srivastava,et al.  Universal Analytical Forms for Modeling Image Probabilities , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[33]  Jan Sokolowski,et al.  Introduction to shape optimization , 1992 .

[34]  Tony F. Chan,et al.  A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model , 2002, International Journal of Computer Vision.

[35]  Michel Barlaud,et al.  DREAM2S: Deformable Regions Driven by an Eulerian Accurate Minimization Method for Image and Video Segmentation , 2002, International Journal of Computer Vision.

[36]  David Mumford,et al.  Statistics of natural images and models , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[37]  Joachim M. Buhmann,et al.  Empirical evaluation of dissimilarity measures for color and texture , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[38]  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..

[39]  J. Simon.  Differentiation with Respect to the Domain in Boundary Value Problems , 1980 .

[40]  Jitendra Malik,et al.  Normalized Cuts and Image Segmentation , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[41]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[42]  J. H. Hateren,et al.  Independent component filters of natural images compared with simple cells in primary visual cortex , 1998 .

[43]  Song-Chun Zhu,et al.  Exploring Texture Ensembles by Efficient Markov Chain Monte Carlo-Toward a 'Trichromacy' Theory of Texture , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[44]  Eero P. Simoncelli,et al.  Random Cascades on Wavelet Trees and Their Use in Analyzing and Modeling Natural Images , 2001 .

[45]  PortillaJavier,et al.  A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients , 2000 .

[46]  Song-Chun Zhu,et al.  Equivalence of Julesz Ensembles and FRAME Models , 2000, International Journal of Computer Vision.

[47]  David Mumford,et al.  Bayesian Rationale for the Variational Formulation , 1994, Geometry-Driven Diffusion in Computer Vision.

[48]  Eero P. Simoncelli,et al.  Image compression via joint statistical characterization in the wavelet domain , 1999, IEEE Trans. Image Process..

[49]  Jitendra Malik,et al.  A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.