Category Independent Object Proposals

We propose a category-independent method to produce a bag of regions and rank them, such that top-ranked regions are likely to be good segmentations of different objects. Our key objectives are completeness and diversity: every object should have at least one good proposed region, and a diverse set should be top-ranked. Our approach is to generate a set of segmentations by performing graph cuts based on a seed region and a learned affinity function. Then, the regions are ranked using structured learning based on various cues. Our experiments on BSDS and PASCAL VOC 2008 demonstrate our ability to find most objects within a small bag of proposed regions.

[1]  L. Jakobson,et al.  A neurological dissociation between perceiving objects and grasping them , 1991, Nature.

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

[3]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[4]  Andrew Blake,et al.  "GrabCut": interactive foreground extraction using iterated graph cuts , 2004, ACM Trans. Graph..

[5]  Alexei A. Efros,et al.  Geometric context from a single image , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[6]  Jianguo Zhang,et al.  The PASCAL Visual Object Classes Challenge , 2006 .

[7]  Alexei A. Efros,et al.  Recovering Surface Layout from an Image , 2007, International Journal of Computer Vision.

[8]  Alexei A. Efros,et al.  Using Multiple Segmentations to Discover Objects and their Extent in Image Collections , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[9]  Luc Van Gool,et al.  The 2005 PASCAL Visual Object Classes Challenge , 2005, MLCW.

[10]  Ronen Basri,et al.  Hierarchy and adaptivity in segmenting visual scenes , 2006, Nature.

[11]  Christof Koch,et al.  Modeling attention to salient proto-objects , 2006, Neural Networks.

[12]  Antonio Torralba,et al.  LabelMe: A Database and Web-Based Tool for Image Annotation , 2008, International Journal of Computer Vision.

[13]  Andrew Zisserman,et al.  An Exemplar Model for Learning Object Classes , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  Bernt Schiele,et al.  Robust Object Detection with Interleaved Categorization and Segmentation , 2008, International Journal of Computer Vision.

[15]  Alexei A. Efros,et al.  Recovering Occlusion Boundaries from a Single Image , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[16]  Alexei A. Efros,et al.  Improving Spatial Support for Objects via Multiple Segmentations , 2007, BMVC.

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

[18]  Andrew J. Davison,et al.  Active Matching , 2008, ECCV.

[19]  Martial Hebert,et al.  Towards unsupervised whole-object segmentation: Combining automated matting with boundary detection , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[20]  David A. McAllester,et al.  A discriminatively trained, multiscale, deformable part model , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[21]  Derek Hoiem,et al.  Learning CRFs Using Graph Cuts , 2008, ECCV.

[22]  Christoph H. Lampert,et al.  Learning to detect unseen object classes by between-class attribute transfer , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[23]  Joseph J. Lim,et al.  Recognition using regions , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[24]  Jitendra Malik,et al.  Object detection using a max-margin Hough transform , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[25]  Jitendra Malik,et al.  From contours to regions: An empirical evaluation , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[26]  Luc Van Gool,et al.  The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.

[27]  Ali Farhadi,et al.  Describing objects by their attributes , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[28]  Andrew Zisserman,et al.  Multiple kernels for object detection , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[29]  Stephen Gould,et al.  Decomposing a scene into geometric and semantically consistent regions , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[30]  Thomas Deselaers,et al.  What is an object? , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[31]  Cristian Sminchisescu,et al.  Constrained parametric min-cuts for automatic object segmentation , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[32]  Alexei A. Efros,et al.  Recovering Occlusion Boundaries from an Image , 2011, International Journal of Computer Vision.