Group saliency propagation for large scale and quick image co-segmentation

Most of the existing co-segmentation methods are usually complex, and require pre-grouping of images, fine-tuning a few parameters and initial segmentation masks etc. These limitations become serious concerns for their application on large scale datasets. In this paper, Group Saliency Propagation (GSP) model is proposed where a single group saliency map is developed, which can be propagated to segment the entire group. In addition, it is also shown how a pool of these group saliency maps can help in quickly segmenting new input images. Experiments demonstrate that the proposed method can achieve competitive performance on several benchmark co-segmentation datasets including ImageNet, with the added advantage of speed up.

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

[2]  Ce Liu,et al.  Unsupervised Joint Object Discovery and Segmentation in Internet Images , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

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

[4]  Andrew Blake,et al.  Cosegmentation of Image Pairs by Histogram Matching - Incorporating a Global Constraint into MRFs , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

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

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

[7]  Matthieu Guillaumin,et al.  ImageNet Auto-Annotation with Segmentation Propagation , 2014, International Journal of Computer Vision.

[8]  Jean Ponce,et al.  Multi-class cosegmentation , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Jean Ponce,et al.  Discriminative clustering for image co-segmentation , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[10]  Jianfei Cai,et al.  Automatic image co-segmentation using geometric mean saliency , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[11]  Antonio Torralba,et al.  Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope , 2001, International Journal of Computer Vision.

[12]  Antonio Torralba,et al.  SIFT Flow: Dense Correspondence across Scenes and Its Applications , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Eric P. Xing,et al.  On multiple foreground cosegmentation , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  Matthieu Guillaumin,et al.  Segmentation Propagation in ImageNet , 2012, ECCV.

[15]  S. Süsstrunk,et al.  SLIC Superpixels ? , 2010 .

[16]  Jiebo Luo,et al.  iCoseg: Interactive co-segmentation with intelligent scribble guidance , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

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

[18]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[19]  Takeo Kanade,et al.  Distributed cosegmentation via submodular optimization on anisotropic diffusion , 2011, 2011 International Conference on Computer Vision.

[20]  Antonio Criminisi,et al.  TextonBoost: Joint Appearance, Shape and Context Modeling for Multi-class Object Recognition and Segmentation , 2006, ECCV.