auto-context and its application to high-level vision tasks
DESCRIPTION
Auto-Context and Its Application to High-level Vision Tasks. Zhuowen Tu CVPR 2008 Presented by Vladimir Reilly. Problems Tackled in Paper. Horse Segmentation Label Every pixel in image as horse or background. Problems Tackled in Paper. Image labeling More complex segmentation. - PowerPoint PPT PresentationTRANSCRIPT
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Auto-Context and Its Application to High-level Vision Tasks
Zhuowen Tu CVPR 2008Presented by Vladimir Reilly
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Problems Tackled in Paper Horse Segmentation
Label Every pixel in image as horse or background
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Problems Tackled in Paper Image labeling
More complex segmentation
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Problems Tackled in Paper Human body Segmentation
Label Body Parts
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Solution Context ADABOOST
Cool Idea Contextual information is integrated directly into
ADABOOST Context not limited by spatial proximity Fast General
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Context
Appearance Context
Label Context
?
Tree?
Grass?
Sky?
Human?
Grass
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Previous Work CRFs
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Previous Work Spatial Boost
In addition to appearance InformationLook at labels of neighbor pixels
Derive weak Spatial Learner
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The Algorithm Iteration 1
Train Image Label Map
Extract 21x21patch
Generate Weak Appearance
Learners8000 possible features
Train Strong Classifier
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The Algorithm Iteration > 1
Train Image Label Map
Segment Images
Probability Map
Extract 21x21patch
Generate Weak Appearance
Learners8000 possible features
Generate Weak Context Learners
Shoot RaysSample Along RaysCompute Statistics
4000 possible features
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Probability out of adaboost
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PBT
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Results
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Results
Google Images
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Interesting Observations Starting with second classifier
90% of selected learners are context learners Label Context improves results Appearance Context worsens results
Probability Map
Train Image
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Results
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Results
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Results