Point-Cut: Interactive Image Segmentation Using Point Supervision

Interactive image segmentation is a fundamental task in many applications in graphics, image processing, and computational photography. Many leading methods formulate elaborated energy functionals, achieving high performance with reflecting human’s intention. However, they show limitations in practical usage since user interaction is labor intensive to obtain segments efficiently. We present an interactive segmentation method to handle this problem. Our approach, called point cut, requires minimal point supervision only. To this end, we use off-the-shelf object proposal methods that generate object candidates with high recall. With the single point supervision, foreground appearance can be estimated with high accuracy, and then integrated into a graph cut optimization to generate binary segments. Intensive experiments show that our approach outperforms existing methods for interactive object segmentation both qualitatively and quantitatively.

[1]  Bo Han,et al.  TouchCut: Fast image and video segmentation using single-touch interaction , 2014, Comput. Vis. Image Underst..

[2]  Gabriel Taubin,et al.  Laplacian Coordinates for Seeded Image Segmentation , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Jia Xu,et al.  Incorporating User Interaction and Topological Constraints within Contour Completion via Discrete Calculus , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Koen E. A. van de Sande,et al.  Selective Search for Object Recognition , 2013, International Journal of Computer Vision.

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

[6]  Vladlen Koltun,et al.  Geodesic Object Proposals , 2014, ECCV.

[7]  Sang Uk Lee,et al.  Generative Image Segmentation Using Random Walks with Restart , 2008, ECCV.

[8]  David Salesin,et al.  A Bayesian approach to digital matting , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[9]  Cristian Sminchisescu,et al.  CPMC: Automatic Object Segmentation Using Constrained Parametric Min-Cuts , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Horst Bischof,et al.  Interactive Multi-label Segmentation , 2010, ACCV.

[11]  Junjie Bai,et al.  Error-Tolerant Scribbles Based Interactive Image Segmentation , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Vladimir Kolmogorov,et al.  "GrabCut": interactive foreground extraction using iterated graph cuts , 2004, ACM Trans. Graph..

[13]  William A. Barrett,et al.  Intelligent scissors for image composition , 1995, SIGGRAPH.

[14]  Nanning Zheng,et al.  Learning to Detect a Salient Object , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Carsten Rother,et al.  DenseCut: Densely Connected CRFs for Realtime GrabCut , 2015, Comput. Graph. Forum.

[16]  Cordelia Schmid,et al.  Segmentation Driven Object Detection with Fisher Vectors , 2013, 2013 IEEE International Conference on Computer Vision.

[17]  Ali Borji,et al.  Salient Object Detection: A Benchmark , 2015, IEEE Transactions on Image Processing.

[18]  Fabio Pellacini,et al.  AppProp: all-pairs appearance-space edit propagation , 2008, SIGGRAPH 2008.

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

[20]  Jian Sun,et al.  ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentation , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[21]  L. R. Dice.  Measures of the Amount of Ecologic Association Between Species , 1945 .

[22]  Shi-Min Hu,et al.  Global contrast based salient region detection , 2011, CVPR 2011.

[23]  Dani Lischinski,et al.  Colorization using optimization , 2004, SIGGRAPH 2004.

[24]  Jonathan T. Barron,et al.  Multiscale Combinatorial Grouping , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[25]  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.

[26]  Zhuowen Tu,et al.  MILCut: A Sweeping Line Multiple Instance Learning Paradigm for Interactive Image Segmentation , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

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

[28]  Lena Gorelick,et al.  GrabCut in One Cut , 2013, 2013 IEEE International Conference on Computer Vision.

[29]  Jitendra Malik,et al.  Tracking as Repeated Figure/Ground Segmentation , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[30]  Daniel P. Huttenlocher,et al.  Efficient Graph-Based Image Segmentation , 2004, International Journal of Computer Vision.

[31]  Santiago Manen,et al.  Prime Object Proposals with Randomized Prim's Algorithm , 2013, 2013 IEEE International Conference on Computer Vision.

[32]  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.

[33]  Jian Sun,et al.  BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[34]  Sang Uk Lee,et al.  Nonparametric higher-order learning for interactive segmentation , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.