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Auto-Context and Its Application to High-level Vision Tasks

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.

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Auto-Context and Its Application to High-level Vision Tasks

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  1. Auto-Context and Its Application to High-level Vision Tasks Zhuowen Tu CVPR 2008 Presented by Vladimir Reilly

  2. Problems Tackled in Paper Horse Segmentation Label Every pixel in image as horse or background

  3. Problems Tackled in Paper Image labeling More complex segmentation

  4. Problems Tackled in Paper Human body Segmentation Label Body Parts

  5. Solution • Context • ADABOOST • Cool Idea • Contextual information is integrated directly into ADABOOST • Context not limited by spatial proximity • Fast • General

  6. Context ? Appearance Context Label Context Grass Tree? Grass? Sky? Human?

  7. Previous Work CRFs

  8. Previous Work Spatial Boost In addition to appearance Information Look at labels of neighbor pixels Derive weak Spatial Learner

  9. The Algorithm Iteration 1 Train Image Label Map Train Strong Classifier Generate Weak Appearance Learners Extract 21x21patch 8000 possible features

  10. The Algorithm Iteration > 1 Probability Map Train Image Label Map Shoot Rays Segment Images Sample Along Rays Compute Statistics Generate Weak Appearance Learners Generate Weak Context Learners Extract 21x21patch 4000 possible features 8000 possible features

  11. Probability out of adaboost

  12. PBT

  13. Results

  14. Results Google Images

  15. Interesting Observations • Starting with second classifier • 90% of selected learners are context learners • Label Context improves results • Appearance Context worsens results Train Image Probability Map

  16. Results

  17. Results

  18. Results

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