Image Analysis

We propose a method for weakly supervised segmentation of natural images, which may contain both textured or non-textured regions. Our texture representation is based on a dictionary of image patches. To divide an image into separated regions with similar texture we use an implicit level sets representation of the curve, which makes our method topologically adaptive. In addition, we suggest a multi-label version of the method. Finally, we improve upon a similar texture representation, by formulating the computation of a texture probability in terms of a matrix multiplication. This results in an efficient implementation of our segmentation method. We experimentally validated our approach on a number of natural as well as composed images.

[1]  Jean Ponce,et al.  Task-Driven Dictionary Learning , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Tony F. Chan,et al.  Active Contours without Edges for Vector-Valued Images , 2000, J. Vis. Commun. Image Represent..

[3]  Rachid Deriche,et al.  Universität Des Saarlandes Fachrichtung 6.1 – Mathematik Colour, Texture, and Motion in Level Set Based Segmentation and Tracking Colour, Texture, and Motion in Level Set Based Segmentation and Tracking , 2022 .

[4]  Karl Skretting,et al.  Texture Classification Using Sparse Frame-Based Representations , 2006, EURASIP J. Adv. Signal Process..

[5]  Thomas Brox,et al.  A TV Flow Based Local Scale Measure for Texture Discrimination , 2004, ECCV.

[6]  Anthony J. Yezzi,et al.  A statistical approach to snakes for bimodal and trimodal imagery , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[7]  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.

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

[9]  Rasmus Larsen,et al.  Learning Dictionaries of Discriminative Image Patches , 2011, BMVC.

[10]  Tony F. Chan,et al.  An Active Contour Model without Edges , 1999, Scale-Space.

[11]  Martha Elizabeth Shenton,et al.  Sparse Texture Active Contour , 2013, IEEE Transactions on Image Processing.

[12]  Gabriel Peyré,et al.  Sparse Modeling of Textures , 2009, Journal of Mathematical Imaging and Vision.

[13]  T. Chan,et al.  A fast algorithm for level set based optimization , 2002 .

[14]  T. Chan,et al.  A Variational Level Set Approach to Multiphase Motion , 1996 .

[15]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[16]  V. Caselles,et al.  A geometric model for active contours in image processing , 1993 .

[17]  Baba C. Vemuri,et al.  Shape Modeling with Front Propagation: A Level Set Approach , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Guillermo Sapiro,et al.  Discriminative learned dictionaries for local image analysis , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

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

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

[21]  Roman Goldenberg,et al.  Fast Geodesic Active Contours , 1999, Scale-Space.

[22]  Vladimir Kolmogorov,et al.  An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision , 2001, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  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..

[24]  Lin He,et al.  Solving the Chan-Vese Model by a Multiphase Level Set Algorithm Based on the Topological Derivative , 2007, SSVM.

[25]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[26]  Horst Bischof,et al.  Interactive Texture Segmentation using Random Forests and Total Variation , 2009, BMVC.

[27]  Anders Bjorholm Dahl,et al.  Dictionary Snakes , 2014, 2014 22nd International Conference on Pattern Recognition.

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

[29]  Manik Varma,et al.  Locally Invariant Fractal Features for Statistical Texture Classification , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[30]  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..

[31]  Thomas Brox,et al.  A TV flow based local scale estimate and its application to texture discrimination , 2006, J. Vis. Commun. Image Represent..

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

[33]  Michael Elad,et al.  Sparse and Redundant Representations - From Theory to Applications in Signal and Image Processing , 2010 .

[34]  F. Gibou.  A fast hybrid k-means level set algorithm for segmentation , 2005 .

[35]  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.

[36]  Guillermo Sapiro,et al.  Supervised Dictionary Learning , 2008, NIPS.