Category-Independent Object Proposals with Diverse Ranking

@article{Endres2014CategoryIndependentOP,
  title={Category-Independent Object Proposals with Diverse Ranking},
  author={Ian Endres and Derek Hoiem},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2014},
  volume={36},
  pages={222-234}
}
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… CONTINUE READING

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