Constrained parametric min-cuts for automatic object segmentation

@article{Carreira2010ConstrainedPM,
  title={Constrained parametric min-cuts for automatic object segmentation},
  author={Jo{\~a}o Carreira and Cristian Sminchisescu},
  journal={2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
  year={2010},
  pages={3241-3248}
}
We present a novel framework for generating and ranking plausible objects hypotheses in an image using bottom-up processes and mid-level cues. The object hypotheses are represented as figure-ground segmentations, and are extracted automatically, without prior knowledge about properties of individual object classes, by solving a sequence of constrained parametric min-cut problems (CPMC) on a regular image grid. We then learn to rank the object hypotheses by training a continuous model to predict… CONTINUE READING

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