Interactive image segmentation by matching attributed relational graphs

A model-based graph matching approach is proposed for interactive image segmentation. It starts from an over-segmentation of the input image, exploiting color and spatial information among regions to propagate the labels from the regions marked by the user-provided seeds to the entire image. The region merging procedure is performed by matching two graphs: the input graph, representing the entire image; and the model graph, representing only the marked regions. The optimization is based on discrete search using deformed graphs to efficiently evaluate the spatial information. Note that by using a model-based approach, different interactive segmentation problems can be tackled: binary and multi-label segmentation of single images as well as of multiple similar images. Successful results for all these cases are presented, in addition to a comparison between our binary segmentation results and those obtained with state-of-the-art approaches. An implementation is available at http://structuralsegm.sourceforge.net/.

[1]  Narendra Ahuja,et al.  Unsupervised Category Modeling, Recognition, and Segmentation in Images , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Tomasz Adamek,et al.  Using contour information and segmentation for object registration, modeling and retrieval , 2006 .

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

[4]  Edward R. Dougherty,et al.  Mathematical Morphology in Image Processing , 1992 .

[5]  Michael F. Cohen,et al.  Optimized Color Sampling for Robust Matting , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Scott Cohen,et al.  Geodesic graph cut for interactive image segmentation , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[7]  Roberto Marcondes Cesar Junior,et al.  Inexact graph matching for model-based recognition: Evaluation and comparison of optimization algorithms , 2005, Pattern Recognit..

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

[9]  Edwin R. Hancock,et al.  Structural Matching by Discrete Relaxation , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Guillermo Sapiro,et al.  A Geodesic Framework for Fast Interactive Image and Video Segmentation and Matting , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[11]  Guillermo Sapiro,et al.  Distancecut: Interactive Segmentation and Matting of Images and Videos , 2007, 2007 IEEE International Conference on Image Processing.

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

[13]  Philippe Salembier,et al.  Binary partition tree as an efficient representation for image processing, segmentation, and information retrieval , 2000, IEEE Trans. Image Process..

[14]  Guoping Qiu,et al.  Interactive Image Segmentation using Optimization with Statistical Priors , 2006 .

[15]  Jean Ponce,et al.  Segmentation by transduction , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  King-Sun Fu,et al.  Error-Correcting Isomorphisms of Attributed Relational Graphs for Pattern Analysis , 1979, IEEE Transactions on Systems, Man, and Cybernetics.

[17]  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.

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

[19]  B. Ripley,et al.  Pattern Recognition , 1968, Nature.

[20]  Guillermo Sapiro,et al.  Interactive Image Segmentation via Adaptive Weighted Distances , 2007, IEEE Transactions on Image Processing.

[21]  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.

[22]  Roberto Marcondes Cesar Junior,et al.  Structural matching of 2D electrophoresis gels using deformed graphs , 2011, Pattern Recognit. Lett..

[23]  Raúl Rojas,et al.  SIOX: simple interactive object extraction in still images , 2005, Seventh IEEE International Symposium on Multimedia (ISM'05).

[24]  Song Wang,et al.  New benchmark for image segmentation evaluation , 2007, J. Electronic Imaging.

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

[26]  Roberto Marcondes Cesar Junior,et al.  Structural Image Segmentation with Interactive Model Generation , 2007, 2007 IEEE International Conference on Image Processing.

[27]  Larry S. Davis,et al.  Improved fast gauss transform and efficient kernel density estimation , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[28]  Horst Bunke,et al.  Recent developments in graph matching , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[29]  Noel E. O'Connor,et al.  A comparative evaluation of interactive segmentation algorithms , 2010, Pattern Recognit..

[30]  Narendra Ahuja,et al.  Region-Based Hierarchical Image Matching , 2008, International Journal of Computer Vision.

[31]  Guillermo Sapiro,et al.  Geodesic Matting: A Framework for Fast Interactive Image and Video Segmentation and Matting , 2009, International Journal of Computer Vision.

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

[33]  David J. Fleet,et al.  Stochastic Tracking of 3D Human Figures Using 2D Image Motion , 2000, ECCV.

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

[35]  Luc Van Gool,et al.  Transductive object cutout , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[36]  Camille Couprie,et al.  Power Watershed: A Unifying Graph-Based Optimization Framework , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.