Adaptive Optimal Shape Prior for Easy Interactive Object Segmentation

For interactive segmentation approaches, object segmentation in complicated background is cumbersome, and usually needs tedious interactions to refine the incomplete segmentations . In this paper, an adaptive optimal shape prior is proposed for easy interactive object segmentation. Different from the traditional shape priors which only provide loose constraint, our adaptive shape prior gives more accurate and individualized constraint by exploiting the shape information of incomplete segmentation. Moreover, by combining the non-rigid shape registration and a local shape consistency evaluation system presented in this paper, such adaptive optimal shape prior could be achieved automatically. Both of these contributions greatly lighten the burden on users and make interactive segmentation much easier. The comparison experiments on the newly-built TypShape dataset with the related algorithms have demonstrated good performance of the proposed algorithm.

[1]  Gareth Funka-Lea,et al.  Automatic heart isolation for CT coronary visualization using graph-cuts , 2006, 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006..

[2]  Wenbing Tao.  Iterative Narrowband-Based Graph Cuts Optimization for Geodesic Active Contours With Region Forces (GACWRF) , 2012, IEEE Transactions on Image Processing.

[3]  Gregory G. Slabaugh,et al.  Graph cuts segmentation using an elliptical shape prior , 2005, IEEE International Conference on Image Processing 2005.

[4]  Vladimir Kolmogorov,et al.  Graph cut based image segmentation with connectivity priors , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[5]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[6]  C. Schmid,et al.  Learning shape prior models for object matching , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

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

[8]  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.

[9]  Jian Sun,et al.  Lazy snapping , 2004, SIGGRAPH 2004.

[10]  Song-Chun Zhu,et al.  Cosegmentation and Cosketch by Unsupervised Learning , 2013, 2013 IEEE International Conference on Computer Vision.

[11]  Pietro Perona,et al.  Unsupervised learning of models for object recognition , 2000 .

[12]  Wenbing Tao,et al.  SaCoseg: Object Cosegmentation by Shape Conformability , 2015, IEEE Transactions on Image Processing.

[13]  Haibin Ling,et al.  Shape Classification Using the Inner-Distance , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[15]  E. Rückert.  Detecting Pedestrians by Learning Shapelet Features , 2007 .

[16]  Pietro Perona,et al.  Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[17]  J. Sethian,et al.  Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations , 1988 .

[18]  Olga Veksler,et al.  Star Shape Prior for Graph-Cut Image Segmentation , 2008, ECCV.

[19]  Wenbing Tao,et al.  Asymmetrical Gauss Mixture Models for Point Sets Matching , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[20]  Joanna Isabelle Olszewska,et al.  Multi-feature vector flow for active contour tracking , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

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

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

[23]  Joshua D. Schwartz,et al.  Hierarchical Matching of Deformable Shapes , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

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

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

[26]  Joanna Isabelle Olszewska,et al.  Embedded double matching of local descriptors for a fast automatic recognition of real-world objects , 2012, 2012 19th IEEE International Conference on Image Processing.

[27]  Kristen Grauman,et al.  Shape Sharing for Object Segmentation , 2012, ECCV.

[28]  Toby Sharp,et al.  Image segmentation with a bounding box prior , 2009, 2009 IEEE 12th International Conference on Computer Vision.

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

[30]  Andrew Blake,et al.  Geodesic star convexity for interactive image segmentation , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[31]  Leo Grady,et al.  P-brush: Continuous valued MRFs with normed pairwise distributions for image segmentation , 2009, CVPR.

[32]  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..

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

[34]  Wenbing Tao,et al.  Multiple piecewise constant with geodesic active contours (MPC-GAC) framework for interactive image segmentation using graph cut optimization , 2011, Image Vis. Comput..

[35]  Pushmeet Kohli,et al.  Dynamic Graph Cuts for Efficient Inference in Markov Random Fields , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[36]  Andriy Myronenko,et al.  Point Set Registration: Coherent Point Drift , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[37]  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).

[38]  Jitendra Malik,et al.  Shape matching and object recognition using shape contexts , 2010, 2010 3rd International Conference on Computer Science and Information Technology.

[39]  G. Griffin,et al.  Caltech-256 Object Category Dataset , 2007 .

[40]  Xiao Han,et al.  A Topology Preserving Level Set Method for Geometric Deformable Models , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[41]  King Ngi Ngan,et al.  From Logo to Object Segmentation , 2013, IEEE Transactions on Multimedia.

[42]  Longin Jan Latecki,et al.  From partial shape matching through local deformation to robust global shape similarity for object detection , 2011, CVPR 2011.

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

[44]  Wenbing Tao,et al.  Image segmentation by iterative optimization of multiphase multiple piecewise constant model and Four-Color relabeling , 2011, Pattern Recognit..

[45]  Ben Taskar,et al.  Object detection via boundary structure segmentation , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[46]  David A. McAllester,et al.  Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[47]  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).

[48]  Yue Gao,et al.  Representative Discovery of Structure Cues for Weakly-Supervised Image Segmentation , 2014, IEEE Transactions on Multimedia.

[49]  Daniel P. Huttenlocher,et al.  Comparing Images Using the Hausdorff Distance , 1993, IEEE Trans. Pattern Anal. Mach. Intell..