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Object Class Recognition by Unsupervised Scale-Invariant Learning

Learn how to enable computers to recognize different object categories in images using generative probabilistic models and scale-invariant learning techniques.

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Object Class Recognition by Unsupervised Scale-Invariant Learning

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  1. Object Class Recognitionby Unsupervised Scale-Invariant Learning R. Fergus, P. Perona, and A. Zisserman Presented By Jeff

  2. Goal: • Enable Computers to Recognize Different Categories of Objects in Images.

  3. Components • Model • Generative Probabilistic Model • Location, Scale, and Appearance • Learning • Estimate Parameters Via EM • Recognition • Evaluate Image Using Model and Threshold

  4. Yuille, ‘91 • Brunelli & Poggio, ‘93 • Lades, v.d. Malsburg et al. ‘93 • Cootes, Lanitis, Taylor et al. ‘95 • Amit & Geman, ‘95, ‘99 • Perona et al. ‘95, ‘96, ’98, ‘00 Model: Constellation Of Parts Fischler & Elschlager, 1973

  5. Parts Selected by Interest Operator • Kadir and Brady's Interest Operator. • Finds Maxima in Entropy Over Scale and Location

  6. c1 c2 Representation of Appearance Projection onto PCA basis 11x11 patch Normalize c15

  7. Generative Probabilistic Model Start with Recognition:

  8. Appearance Gaussian Part Appearance PDF Gaussian Appearance PDF

  9. Shape Gaussian Shape PDF Uniform Shape PDF

  10. 0.8 0.75 0.9 Scale Uniform Relative Scale PDF Gaussian Relative Scale PDF Log(scale) Log(scale) Prob. of detection Poission PDF On # Detections

  11. Occlusion and Part Statistics

  12. Learning • Train Model Parameters Using EM: • Optimize Parameters • Optimize Assignments • Repeat Until Convergence

  13. Recognition Make This: Greater Than Threshold

  14. RESULTS

  15. Motorbikes Equal error rate: 7.5%

  16. Background Images

  17. Frontal faces Equal error rate: 4.6%

  18. Equal error rate: 9.8% Airplanes

  19. Scale-Invariant Cats Equal error rate: 10.0%

  20. Scale-Invariant cars Equal error rate: 9.7%

  21. Robustness of Algorithm

  22. ROC equal error rates Pre-Scaled Data (Identical Settings): Scale-Invariant Learning and Recognition:

  23. Scale-Invariant Cars

  24. The End

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