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Constrained Parametric Min-Cuts for Automatic Object Segmentation

Constrained Parametric Min-Cuts for Automatic Object Segmentation. SasiKanth Bendapudi Yogeshwar Nagaraj. What is a ‘Good Segmentation’?. http:// www.eecs.berkeley.edu /Research/ Projects /CS/vision/grouping/ resources.html. “Geometric context from a single image”, Hoiem et al. , ICCV 2005.

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Constrained Parametric Min-Cuts for Automatic Object Segmentation

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  1. Constrained Parametric Min-Cuts for Automatic Object Segmentation SasiKanthBendapudi YogeshwarNagaraj

  2. What is a ‘Good Segmentation’?

  3. http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/resources.htmlhttp://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/resources.html

  4. “Geometric context from a single image”, Hoiemet al., ICCV 2005

  5. “Using Multiple Segmentations to Discover Objects and their Extent in Image Collections”, Russelet al., CVPR 2006

  6. “Improving Spatial Support for Objects via Multiple Segmentations”, Malisiewicz& Efros, BMVC 2007

  7. “Towards Unsupervised Whole-Object Segmentation: Combining Automated Matting with Boundary Detection”, Stein & Hebert, CVPR 2008

  8. The Paper • Figure-Ground segmentation • Solve CPMC by minimizing the objective function using various seeds and parameters • Reject redundancies and obvious negatives based on segment energies and similarities • Learn the characteristics of a ‘Figure’ segment to qualitatively assess the remaining segments

  9. Objective Function

  10. Objective Function

  11. Objective Function

  12. Synthetic Example

  13. Synthetic Example

  14. Initialization • Foreground • Regular 5x5 grid geometry • Centroids of large N-Cuts regions • Centroids of superpixels closest to grid positions • Background • Full image boundary • Horizontal boundaries • Vertical boundaries • All boundaries excluding the bottom one Performance broadly invariant to different initializations

  15. Fast Rejection Large set of initial segmentations (~5500) Low Energy High Energy ~2000 segments with the lowest energy Cluster segments based on spatial overlap Lowest energy member of each cluster (~154)

  16. Segment Ranking • Model data using a host of features • Graph partition properties • Region properties • Gestalt properties • Train a regressor with the largest overlap ground-truth segment using Random Forests • Diversify similar rankings using Maximal Marginal Relevance (MMR)

  17. Graph Partition Properties • Cut – Sum of affinities along segment boundary • Ratio Cut – Sum along boundary divided by the number • Normalized Cut – Sum of cut and affinity in foreground and background • Unbalanced N-cut – N-cut divided by foreground affinity • Thresholded boundary fraction of a cut

  18. Region Properties • Area • Perimeter • Relative Centroid • Bounding Box properties • Fitting Ellipse properties • Eccentricity • Orientation • Convex Area • Euler Number • Diameter of Circle with the same area of the segment • Percentage of bounding box covered • Absolute distance to the center of the image

  19. Gestalt Properties • Inter-region texton similarity • Intra-region texton similarity • Inter-region brightness similarity • Intra-region brightness similarity • Inter-region contour energy • Intra-region contour energy • Curvilinear continuity • Convexity – Ratio of foreground area to convex hull area

  20. Feature Importance

  21. Feature Importance

  22. Feature Importance

  23. What has been modeled?

  24. Databases • Weizmann database • F-measure criterion • MSR-Cambridge database & Pascal VOC2009 • Segmentation covering

  25. Performance

  26. Test of the algorithm • Berkeley segmentation dataset • Complete pool of images collected • Ranked using the ranking methodology • Top ranks evaluated to test the ranking procedure • How well does the algorithm perform?

  27. Berkeley Database Rank 269!

  28. Berkeley Database Rank 142!

  29. Berkeley Database Rank 98!

  30. Berkeley Database • Compute the Segment Covering score for the top 40 segments of each image in the database

  31. Conclusion • Does Constrained Parametric Min-Cuts work well? • Yes • Does Fast Rejection work well? • Yes • Does Segment Ranking work well? • I don’t think so

  32. Interesting follow up Image Segmentation by Figure-Ground Composition into Maximal Clique, Ion, Carreira, Sminchisescu, ICCV 2011

  33. Interesting follow up • Obtain pool of FG segmentations from CPMC • Define tiling and a probabilistic model for the same • Represent the probabilistic models using mid-level features • Compute and rank various tilingsby implementing discrete searches from each of the nodes Image Segmentation by Figure-Ground Composition into Maximal Clique, Ion, Carreira, Sminchisescu, ICCV 2011

  34. Interesting follow up Image Segmentation by Figure-Ground Composition into Maximal Clique, Ion, Carreira, Sminchisescu, ICCV 2011

  35. Questions?

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