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Texture

Texture. We would like to thank Amnon Drory for this deck. הבהרה: החומר המחייב הוא החומר הנלמד בכיתה ולא זה המופיע / לא מופיע במצגת. . Syllabus. Textons TextonsBoost. Textons. Run filter bank on images Build Texton dictionary using K-means Map texture image to histogram

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Texture

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  1. Texture We would like to thank Amnon Drory for this deck הבהרה: החומר המחייב הוא החומר הנלמד בכיתה ולא זה המופיע / לא מופיע במצגת.

  2. Syllabus Textons TextonsBoost

  3. Textons Run filter bank on images Build Texton dictionary using K-means Map texture image to histogram Histogram Similarity using Chi-square

  4. TextonBoost Build Texton dictionary Texture Layout (pixel, rectangle, Texton) Count number of textons in rectangle Use Integral Image Generate multiple Texture layouts (Features) For each class do 1-vs-all classifier: For each pixel in class Train GentleBoost Classifier Map strong classifier to probability Take Maximum value

  5. CRF/MRF How to ensure Spatial Consistency? ML Likelihood Posterior Bayes Prior MAP

  6. Semantic Texton Forest Decision Trees Forest and Averaging Split decision to minimize Entropy Two level STF to add spatial regularization Works well when there is ample data, does not generalize well

  7. (1) TextonsB. Julesz, Leung, MalikM. Varma, A. Zisserman(II) TextonBoost J. Shotton, J. Winn, C. Rother, A. Criminisi(III) Semantic Texton ForestsJ. Shotton, M. Johnson, R. Cipolla(IV) Pose Recognition from Depth Images J. Shotton, A. Fitzgibbon, M. Cook, T. Sharp, M. Finocchio, R. Moore, A. Kipman, A. Blake

  8. Textures

  9. Filter Bank

  10. K-means

  11. Texton Histogram

  12. Classification

  13. Results

  14. TextonBoost:Joint Appearance, Shape and Context Modeling for Multi-Class Object Recognition and Segmentation J. Shotton*, J. Winn†, C. Rother†, and A. Criminisi† * University of Cambridge † Microsoft Research Ltd, Cambridge, UK

  15. TextonBoost • Simultaneous recognition and segmentation • Explain every pixel

  16. TextonBoost • Input: • Training: Images with pixel level ground truth classification MSRC 21 Database

  17. TextonBoost • Input: • Training: Images with pixel level ground truth classification. • Testing: Images • Output: • A classification of each pixel in the test images to an object class.

  18. Conditional Random Field Unary Term Binary Term Unary Term Binary Term

  19. CRF: Unary Term 0.001 0.47 0.23 0.02 0.1

  20. CRF: Binary Term

  21. CRF: Binary Term • Potts model • encourages neighbouring pixels to have same label • Contrast sensitivity • encourages segmentation to follow image edges

  22. Accurate Segmentation? • Boosted classifier alone • effectively recognises objects • but not sufficient for pixel-perfect segmentation • Conditional Random Field (CRF) • jointly classifies all pixels whilstrespecting image edges unary term only CRF

  23. The TextonBoost CRF Unary Term Texture-Layout Color location edge Binary Term

  24. Location Term Texture-Layout Color location edge • Capture prior on absolute image location tree sky road

  25. Color Term Texture-Layout Color location edge

  26. Texture-Layout Term

  27. Input image Textons • Shape filters use texton maps [Varma & Zisserman IJCV 05] [Leung & Malik IJCV 01] • Compact and efficient characterisation of local texture Clustering  Texton map Colors  Texton Indices Filter Bank

  28. Texture Layout (Toy Example)

  29. , , ( ( ) ) Texture-Layout Filters up to 200 pixels • Pair: • Feature responses v(i, r, t) • Large bounding boxes enablelong range interactions , ( ) v(i1, r, t) = a rectangle r texton t v(i3, r, t) = a/2

  30. Texton Boost - Summary Performs per-pixel classification using: 1. Statistics learned from Training Set: - Absolute location statistics - Configuration of textured areas around pixel of interest. 2. Cues from the Test Image: - Edges - Object Colors 3. Priors.

  31. Results on 21-Class Database building

  32. Effect of Model Components Shape-texture potentials only: 69.6% + edge potentials: 70.3% + Color potentials: 72.0% + location potentials: 72.2% shape-texture + edge + Color & location pixel-wise segmentation accuracies

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