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Object Recognizing

Object Recognizing. Recognition -- topics. Features Classifiers Example ‘winning’ system. Object Classes. Individual Recognition. Object parts Automatic, or query-driven. Window. Mirror. Window. Door knob. Headlight. Back wheel. Bumper. Front wheel. Headlight.

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Object Recognizing

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  1. Object Recognizing

  2. Recognition -- topics • Features • Classifiers • Example ‘winning’ system

  3. Object Classes

  4. Individual Recognition

  5. Object partsAutomatic, or query-driven Window Mirror Window Door knob Headlight Back wheel Bumper Front wheel Headlight

  6. Class Non-class

  7. Variability of Airplanes Detected

  8. Class Non-class

  9. Features and Classifiers Same features with different classifiers Same classifier with different features

  10. Generic Features:The same for all classes Simple (wavelets) Complex (Geons)

  11. Class-specific Features: Common Building Blocks

  12. Optimal Class Components? • Large features are too rare • Small features are found everywhere Find features that carry the highest amount of information

  13. Entropy Entropy: x = 0 1 H p = 0.5 0.5 ? 0.1 0.9 0.47 0.01 0.99 0.08

  14. Mutual information H(C) F=0 F=1 H(C) when F=1 H(C) when F=0 I(C;F) = H(C) – H(C/F)

  15. Mutual Information I(C,F) Class: 1 1 0 1 0 1 0 0 Feature: 1 0 0 1 1 1 0 0 I(F,C) = H(C) – H(C|F)

  16. Optimal classification features • Theoretically: maximizing delivered information minimizes classification error • In practice: informative object components can be identified in training images

  17. Selecting Fragments

  18. Horse-class features Car-class features Pictorial features Learned from examples

  19. Star model Detected fragments ‘vote’ for the center location Find location with maximal vote In variations, a popular state-of-the art scheme

  20. Bag of words

  21. Bag of visual words A large collection of image patches

  22. Generate a dictionary using K-means clustering

  23. – – Recognition by Bag of Words (BoD): Each class has its words historgram Limited or no Geometry Simple and popular, no longer state-of-the art.

  24. HoG Descriptor Dallal, N & Triggs, B. Histograms of Oriented Gradients for Human Detection

  25. Shape context

  26. Recognition Class II: SVM Example Classifiers

  27. SVM – linear separation in feature space

  28. +1 The Margin -1 0 Separating line: w ∙ x + b = 0 Far line: w ∙ x + b = +1 Their distance: w ∙ ∆x = +1 Separation: |∆x| = 1/|w| Margin: 2/|w|

  29. Max Margin Classification The examples are vectors xi The labels yi are +1 for class, -1 for non-class (Equivalently, usually used How to solve such constraint optimization?

  30. Solving the SVM problem • Duality • Final form • Efficient solution • Extensions

  31. Using Lagrange multipliers: Using Lagrange multipliers: Minimize LP = With αi > 0 the Lagrange multipliers

  32. Minimizing the Lagrangian Minimize Lp : Set all derivatives to 0: Also for the derivative w.r.t. αi Dual formulation: Maximize the Lagrangian w.r.t. the αi and the above two conditions.

  33. Solved in ‘dual’ formulation Maximize w.r.t αi : With the conditions:

  34. Dual formulation Mathematically equivalent formulation: Can maximize the Lagrangian with respect to the αi After manipulations – concise matrix form:

  35. Summary points • Linear separation with the largest margin, f(x) = w∙x + b • Dual formulation • Natural extension to non-separable classes • Extension through kernels, f(x) = ∑αi yi K(xi x) + b

  36. Felzenszwalb • Felzenszwalb, McAllester, Ramanan CVPR 2008. A Discriminatively Trained, Multiscale, Deformable Part Model • Many implementation details, will describe the main points.

  37. Using patches with HoG descriptors and classification by SVM Person model HoG orientations with w > 0

  38. Object model using HoG A bicycle and its ‘root filter’ The root filter is a patch of HoG descriptor Image is partitioned into 8x8 pixel cells In each block we compute a histogram of gradient orientations

  39. Dealing with scale: multi-scale analysis The filter is searched on a pyramid of HoG descriptors, to deal with unknown scale

  40. Adding Parts A part Pi = (Fi, vi, si, ai, bi). Fi is filter for the i-th part, vi is the center for a box of possible positions for part i relative to the root position, si the size of this box ai and bi are two-dimensional vectors specifying coefficients of a quadratic function measuring a score for each possible placement of the i-th part. That is, ai and bi are two numbers each, and the penalty for deviation ∆x, ∆y from the expected location is a1 ∆x + a2 ∆y+ b1 ∆x2 + b2 ∆y2

  41. Bicycle model: root, parts, spatial map Person model

  42. Match Score The full score of a potential match is: ∑ Fi ∙ Hi + ∑ ai1 xi + ai2 yi+ bi1xi2 + bi2yi2 Fi ∙ Hi is the appearance part xi, yi, is the deviation of part pi from its expected location in the model. This is the spatial part.

  43. Using SVM: The score of a match can be expressed as the dot-product of a vector β of coefficients, with the image: Score = β∙ψ Using the vectors ψ to train an SVM classifier: β∙ψ > 1 for class examples β∙ψ < 1 for class examples

  44. β∙ψ > 1 for class examples β∙ψ < 1 for class examples However, ψ depends on the placement z, that is, the values of ∆xi, ∆yi We need to take the best ψ over all placements. In their notation: Classification then uses β∙f > 1 We need to take the best ψ over all placements. In their notation: Classification then uses β∙f > 1

  45. Recognition search with gradient descent over the placement. This includes also the levels in the hierarchy. Start with the root filter, find places of high score for it. For these high-scoring locations, each for the optimal placement of the parts at a level with twice the resolution as the root-filter, using GD. Essentially maximize ∑ Fi Hi + ∑ ai1 xi + ai2 y + bi1x2 + bi2y2 Over placements (xi yi) Final decision β∙ψ > θ implies class

  46. Training -- positive examples with bounding boxes around the objects, and negative examples. • Learn root filter using SVM • Define fixed number of parts, at locations of high energy in the root filter HoG • Use these to start the iterative learning

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