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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. - PowerPoint PPT Presentation
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S
Constrained Parametric Min-Cuts for
Automatic Object Segmentation
SasiKanth BendapudiYogeshwar 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
“Using Multiple Segmentations to Discover Objects and their Extent in Image Collections”, Russel et al., CVPR 2006
“Improving Spatial Support for Objects via Multiple Segmentations”, Malisiewicz & Efros, BMVC 2007
“Towards Unsupervised Whole-Object Segmentation: Combining Automated Matting with Boundary Detection”, Stein & Hebert,
CVPR 2008
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
Objective Function
Objective Function
Objective Function
Synthetic Example
Synthetic Example
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
Fast RejectionLarge set of initial segmentations (~5500)
High Energy Low Energy
~2000 segments with the lowest energy
Cluster segments based on spatial overlap
Lowest energy member of each cluster (~154)
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)
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
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
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
Feature Importance
Feature Importance
Feature Importance
What has been modeled?
Databases
Weizmann database F-measure criterion
MSR-Cambridge database & Pascal VOC2009 Segmentation covering
Performance
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?
Berkeley Database
Rank 269!
Berkeley Database
Rank 142!
Berkeley Database
Rank 98!
Berkeley Database
Compute the Segment Covering score for the top 40 segments of each image in the database
Database Segment Covering Score (Top 40)
BSDS 0.52MSR Cambridge 0.77
Pascal VOC 0.63Database Segment Covering Score
(All segments)BSDS 0.61
MSR Cambridge 0.85Pascal VOC 0.78
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
Interesting follow up
Image Segmentation by Figure-Ground Composition into Maximal Clique, Ion, Carreira, Sminchisescu, ICCV 2011
Interesting follow up
Image Segmentation by Figure-Ground Composition into Maximal Clique, Ion, Carreira, Sminchisescu, ICCV 2011
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 tilings by implementing discrete searches from each of the nodes
Interesting follow up
Image Segmentation by Figure-Ground Composition into Maximal Clique, Ion, Carreira, Sminchisescu, ICCV 2011
Questions?