Sunday, June 13, 2010

constrained parametric min-cuts: exciting segmentation for the sake of recognition

I would like to introduce two papers about Constrained Parametric Min-Cuts from C. Sminchisescu's group.  These papers are very relevant to my research direction (which lies at the intersection of segmentation and recognition).  Like my own work, these papers are about segmentation for recognition's sake.  The segmentation algorithm proposed in the paper is a sort of "segment sliding approach", where many binary graph-cuts optimization problems are solved for different Grab-Cut style initializations.  These segments are then scored using a learned scoring function -- think regression versus classification.  They show that these top segments are actually quite meaningful and correspond to object boundaries really well.  Finally a tractable number of top hypothesis (still overlapping at this stage), are piped into a recognition engine.

The idea that features derived from segments are better for recognition than features from the spatial support of a sliding rectangle resonates in all of my papers.  Regarding these CVPR2010 papers, I like their ideas of learning a category-free "segmentation-function" and the sort of multiple-segmentation version of this algorithm is very appealing.  If I remember correctly, the idea of learning a segmentation function comes to us from X. Ren, and the idea of using multiple segmentation comes from D. Hoiem. These papers are a cool new idea utilizing both insights.

J. Carreira and C. Sminchisescu. Constrained Parametric Min-Cuts for Automatic Object Segmentation. In CVPR 2010.

F. Li, J. Carreira, and C. Sminchisescu. Object Recognition as Ranking Holistic Figure-Ground Hypotheses. In CVPR 2010.


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Spotlights for these papers are during these tracks at CVPR2010:
Object Recognition III: Similar Shapes
Segmentation and Grouping II: Semantic Segmentation tracks