Discussion of Pictorial Structures Pedro Felzenszwalb Daniel Huttenlocher Sicily Workshop September,...
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Transcript of Discussion of Pictorial Structures Pedro Felzenszwalb Daniel Huttenlocher Sicily Workshop September,...
Discussion ofPictorial Structures
Pedro FelzenszwalbDaniel Huttenlocher
Sicily WorkshopSeptember, 2006
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What are Pictorial Structures?
Local appearance– Part models– Parts feature detection
Global geometry– Not necessarily fully
connected graph
Joint optimization– Combine appearance
and geometry withouthard constraints• “Stretch and fit”• Qualitative
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Pictorial Structure Models
Parts have match quality at each location– Location in a configuration space
– No feature detection
Maps for parts combined together into overall quality map– According to underlying graph structure
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A History of Pictorial Structures
Fischler and Elschlager original 1973 paper
Burl, Weber and Perona ECCV 1998– Probabilistic formulation
– Full joint Gaussian spatial model
– Computational challenges led to feature-based
Felzenszwalb and Huttenlocher CVPR 2000– Explicit revisiting of FE73 for trees, probabilistic
– Efficient algorithms using distance transforms
Crandall et al CVPR 2005, ECCV 2006– Low tree-width graph structures, unsupervised
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Matching Pictorial Structures
Cost map for each part
Distance transform (soft max) using spatial model
Shift and combine– Localize root then recursively other parts
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Learning Models
Automatically determine which spatial relationships to represent [FH03]
Weakly supervised learning [CH06]– Learn part appearance and geometric relations
simultaneously
– No labeling of part locations
– Use large number of patches, similar to Ullman
– Better detection accuracy than strongly supervised
Car (rear) star topology
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Parts as Context
No part detected without using context provided by other parts– Detect overall configuration composed of parts
in a spatial arrangement
Allows for weak evidence for a part– Unlike feature detection
Combination of matches can constrain pose
In contrast to scene-level context – More spatial regularity
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Factored Models
For n parts in fixed arrangement with k templates per part– Exponential number of possibilities, O(kn)
For variable arrangement, another exponential factor
Important both for representation and algorithmic efficiency
Pictorial structures takes particular advantage of this factoring
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Closely Related Work
Ioffe and Forsyth, Ramanan and Forsyth human body pose– Part detection but very “dense” part locations
Constellation models– Fergus, Perona, Zisserman and others
– Hard feature detection in contrast with BWP98 soft feature matching
Amit’s patch models– No assumption of independent part appearance
Fergus and Zisserman star models
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What’s Important
No decisions until the end– No feature detection
• Quality maps or likelihoods
– No hard geometric constraints• Deformation costs or priors
Efficient algorithms– Dynamic programming critical or can’t get
away without making intermediate decisions
– Not applicable to all problems, need good factorizations of geometry and appearance
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Some Pros
Good for categorical object recognition– Qualitative descriptions of appearance
– Factoring variability in appearance and geometry
Deals well with occlusion– In contrast to hard feature detection
Weakly supervised learning algorithms
Sampling as way of dealing with models that don’t factor – more Saturday
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Some Cons/Limitations
Most applicable to 2D objects defined by relatively small number of parts
Unclear how to extend to large number of transformation parameters per part– Explicit representation grows exponentially
No known way of using to index into model databases
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Role of Spatial Constraints
For k-fans, spatial information substantially improves detection accuracy– However, limited by relatively small number of
parts compared to features in a bag
General question