Category Independent Object Proposals

  • Ian Endres
  • Derek Hoiem
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6315)


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.


Appearance Model Object Region Boost Decision Tree Hierarchical Segmentation Occlusion Boundary 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Ian Endres
    • 1
  • Derek Hoiem
    • 1
  1. 1.Department of Computer ScienceUniversity of Illinois at Urbana-Champaign 

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