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Rgb(d) Scene Labeling- features and algorithms

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Provide a summary for "RGB-(D) Scene Labeling: Features and Algorithms" paper, written by X Ren, L Bo, D Fox - Computer Vision and Pattern Recognition 2012 - ieeexplore.ieee.org

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Rgb(d) Scene Labeling- features and algorithms

  1. 1. RGB-(D) Scene Labeling: Features and Algorithms Ahmed Taha May 2014 Supervised by Dr : Marwan A. Torki
  2. 2.  Introduction  Scene labeling challenges  Pipeline  Feature Extraction  Super-pixel formulation and classification  Classifying segmentation tree paths  Classifying super-pixels MRF  Datasets and results Agenda
  3. 3.  Scene Labeling  Labeling of each pixel in an image to a certain class  Scene Labeling can be done  Indoors  Label a Sofa in a Bedroom  Label a door in a living room  Outdoors  Label a car in street  Label building in street Scene Labeling
  4. 4. Scene Labeling
  5. 5. Scene Labeling
  6. 6.  Indoor scene labeling challenges  Large variations of scene types  Lack of distinctive features  Poor illumination Scene Labeling
  7. 7.  Benefits of using depth feature in scene labeling  Increased accuracy and robustness  Body pose estimation  3D mapping  Object recognition  3D modeling and interaction Scene Labeling
  8. 8. Pipeline
  9. 9. 1. Extract features using Kernel descriptor (KDES). 2. Aggregate descriptors in dense region into super- pixels using Efficient match kernels (EMK) 3. Classify super-pixels using Linear support vector machine (SVM) 4. Label super-pixels by classifying paths of segmentation tree. 5. Label super-pixels using super-pixel MRF Pipeline
  10. 10.  Kernel Descriptors (KDES), a unified framework that uses different aspects of similarity (kernel) to derive patch descriptors.  Image gradient  Spin/normal  Color  Depth gradient Features Extraction (Step 1)
  11. 11.  Efficient match kernels (EMK) to transform and aggregate descriptors in a set S (grid locations in the interior of a superpixel ‘s’).  Super-pixels are not of the same size. Super-pixel formation (Step 2)
  12. 12.  Linear Support vector machine (SVM)  Non-probabilistic binary linear classifier. Classify superpixels (Step 3)
  13. 13.  Classifying paths in segmentation tree Contextual Models (Step 4)
  14. 14.  Classifying paths in segmentation tree Contextual Models (Step 4)
  15. 15.  Classifying paths in segmentation tree Contextual Models
  16. 16.  Classifying paths in segmentation tree  If we accumulate features over paths, the accuracy continues to increase to the top level  The initial part of the curves overlap, suggesting there is little benefit going to superpixels at too fine scales Contextual Models
  17. 17.  Superpixel MRF with gPb Contextual Models (Step 5)
  18. 18.  Superpixel MRF with gPb  standard MRF formulation. We use Graph Cut to find the labeling that mini- mizes the energy of a pairwise MRF Contextual Models (Step 5)
  19. 19. Pipeline
  20. 20.  NYU-D dataset  Improve accuracy from 50% to 76%  Stanford Background dataset  Improve accuracy 79% to 82% Datasets - Results
  21. 21. Datasets - Results
  22. 22.  Rgb-(d) scene labeling: Features and algorithms  X Ren, L Bo, D Fox - Computer Vision and Pattern Recognition 2012 - ieeexplore.ieee.org  Context by region ancestry  JJ Lim, P Arbeláez, C Gu, J Malik - Computer Vision, 2009 IEEE 2009 - ieeexplore.ieee.org References

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