2. How many object categories are there? Biederman 1987

3. So what does object recognition involve?

4. Verification: is that a lamp?

5. Detection: are there people?

6. Identification: is that Potala Palace?

7. Object categorization mountain building tree banner vendor people street lamp

8. Scene and context categorization outdoor city …

9. Computational photography

10. Assisted driving meters Ped Car Lane detection Pedestrian and car detection Collision warning systems with adaptive cruise control, Lane departure warning systems, Rear object detection systems,

11. Improving online search Query: STREET Organizing photo collections

12. Challenges 1: view point variation Michelangelo 1475-1564

13. Challenges 2: illumination slide credit: S. Ullman

14. Challenges 3: occlusion Magritte, 1957

15. Challenges 4: scale

16. Challenges 5: deformation Xu, Beihong 1943

17. Challenges 6: background clutter Klimt, 1913

18. Challenges 7: intra-class variation

20. Issues Representation How to represent image, memory? –Low or high level? Concrete or abstract? –Appearance, shape, or both? –Local or global? –Invariance, dimensionality reduction Memory contact When do you make it? –Bottom up? Top down? –Difficulty of computation

21. Issues Learning Recognition –Discriminative or generative? –Specific object or object category? –One object or many? –Categorization or detection? –Scaling up?

22. Local, low level Representation The image itself

23. Local, low level Representation

24. Representation –Part-based

25. Representation –Part-based

26. The correspondence problem Model with P parts Image with N possible assignments for each part Consider mapping to be 1-1 N P combinations!!!

27. Deformable Template Matching Query Template Berg, Berg and Malik CVPR 2005 Formulate problem as Integer Quadratic Programming O(N P ) in general Use approximations that allow P=50 and N=2550 in <2 secs

28. Jigsaw approach: Borenstein and Ullman, 2002

29. Global representation

30. –Unclear how to model categories, so we learn what distinguishes them rather than manually specify the difference -- hence current interest in machine learning Learning

31. –Scale / orientation range to search over –Speed –Context Recognition

32. Geons –Biederman, description of geons. Are they still view invariant when describing a geon? 3D shape’s occluding contour depends on viewpoint. May be straight from one view, curved from another. Metric properties not truly invariant. Maybe more like quasi- invariants.

33. Geons for Recognition Analogy to speech. –36 different geons. –Different relations between them. –Millions of ways of putting a few geons together.

34. Chamfer Matching For every edge point in the transformed object, compute the distance to the nearest image edge point. Sum distances.

36. Linear subspaces Classification can be expensive –Must either search (e.g., nearest neighbors) or store large PDF’s Suppose the data points are arranged as above Idea—fit a line, classifier measures distance to line convert x into v 1, v 2 coordinates What does the v 2 coordinate measure? What does the v 1 coordinate measure? - distance to line - use it for classification—near 0 for orange pts - position along line - use it to specify which orange point it is

37. Dimensionality reduction How to find v 1 and v 2 ? - work out on board Dimensionality reduction We can represent the orange points with only their v 1 coordinates –since v 2 coordinates are all essentially 0 This makes it much cheaper to store and compare points A bigger deal for higher dimensional problems

38. Linear subspaces Consider the variation along direction v among all of the orange points: What unit vector v minimizes var? What unit vector v maximizes var? Solution:v 1 is eigenvector of A with largest eigenvalue v 2 is eigenvector of A with smallest eigenvalue

39. Principle component analysis Suppose each data point is N-dimensional –Same procedure applies: –The eigenvectors of A define a new coordinate system eigenvector with largest eigenvalue captures the most variation among training vectors x eigenvector with smallest eigenvalue has least variation –We can compress the data by only using the top few eigenvectors corresponds to choosing a “linear subspace” –represent points on a line, plane, or “hyper-plane” these eigenvectors are known as the principle components

40. The space of faces An image is a point in a high dimensional space –An N x M image is a point in R NM –We can define vectors in this space as we did in the 2D case + =

41. Dimensionality reduction The set of faces is a “subspace” of the set of images –Suppose it is K dimensional –We can find the best subspace using PCA –This is like fitting a “hyper-plane” to the set of faces spanned by vectors v 1, v 2,..., v K any face

42. Eigenfaces PCA extracts the eigenvectors of A –Gives a set of vectors v 1, v 2, v 3,... –Each one of these vectors is a direction in face space what do these look like?

43. Projecting onto the eigenfaces The eigenfaces v 1,..., v K span the space of faces –A face is converted to eigenface coordinates by

44. Recognition with eigenfaces Algorithm 1.Process the image database (set of images with labels) Run PCA—compute eigenfaces Calculate the K coefficients for each image 2.Given a new image (to be recognized) x, calculate K coefficients 3.Detect if x is a face 4.If it is a face, who is it? Find closest labeled face in database nearest-neighbor in K-dimensional space

45. Choosing the dimension K KNMi = eigenvalues How many eigenfaces to use? Look at the decay of the eigenvalues –the eigenvalue tells you the amount of variance “in the direction” of that eigenface –ignore eigenfaces with low variance

46. One simple method: skin detection Skin pixels have a distinctive range of colors –Corresponds to region(s) in RGB color space for visualization, only R and G components are shown above skin Skin classifier A pixel X = (R,G,B) is skin if it is in the skin region But how to find this region?

47. Skin detection Learn the skin region from examples –Manually label pixels in one or more “training images” as skin or not skin –Plot the training data in RGB space skin pixels shown in orange, non-skin pixels shown in blue some skin pixels may be outside the region, non-skin pixels inside. Why? Skin classifier Given X = (R,G,B): how to determine if it is skin or not?

48. Skin classification techniques Skin classifier Given X = (R,G,B): how to determine if it is skin or not? Nearest neighbor –find labeled pixel closest to X –choose the label for that pixel Data modeling –fit a model (curve, surface, or volume) to each class Probabilistic data modeling –fit a probability model to each class

49. Probabilistic skin classification Now we can model uncertainty –Each pixel has a probability of being skin or not skin Skin classifier Given X = (R,G,B): how to determine if it is skin or not? Choose interpretation of highest probability – set X to be a skin pixel if and only if Where do we get and ?

50. Learning conditional PDF’s We can calculate P(R | skin) from a set of training images –It is simply a histogram over the pixels in the training images each bin R i contains the proportion of skin pixels with color R i This doesn’t work as well in higher-dimensional spaces. Why not? Approach: fit parametric PDF functions common choice is rotated Gaussian –center –covariance »orientation, size defined by eigenvecs, eigenvals

51. Learning conditional PDF’s We can calculate P(R | skin) from a set of training images –It is simply a histogram over the pixels in the training images each bin R i contains the proportion of skin pixels with color R i But this isn’t quite what we want Why not? How to determine if a pixel is skin? We want P(skin | R) not P(R | skin) How can we get it?

52. Bayes rule In terms of our problem: what we measure (likelihood) domain knowledge (prior) what we want (posterior) normalization term The prior: P(skin) Could use domain knowledge –P(skin) may be larger if we know the image contains a person –for a portrait, P(skin) may be higher for pixels in the center Could learn the prior from the training set. How? –P(skin) may be proportion of skin pixels in training set

53. Skin detection results

54. Object categorization: the statistical viewpoint vs. Bayes rule: posterior ratio likelihood ratioprior ratio

55. Discriminative Direct modeling of Zebra Non-zebra Decision boundary

56. Model and Generative LowMiddle HighMiddle  Low

57. Object Bag of ‘words’

58. Looser definition –Independent features A clarification: definition of “BoW”

59. Feature-detector view

60. No rigorous geometric information of the object components It’s intuitive to most of us that objects are made of parts – no such information Not extensively tested yet for –View point invariance –Scale invariance Segmentation and localization unclear Weakness of the model

61. Part 2: part-based models by Rob Fergus (MIT)

62. Representation

63. Model: Parts and Structure

64. Representation Object as set of parts –Generative representation Model: –Relative locations between parts –Appearance of part Issues: –How to model location –How to represent appearance –Sparse or dense (pixels or regions) –How to handle occlusion/clutter Figure from [Fischler & Elschlager 73]

65. Jigsaw approach Each patch has foreground/background mask

66. Pose: Chamfer Matching with the Distance Transform 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 11 1 1 1 1 2 2 2 2 2 2 2 2 2 2 223 3 3 3 3 3 3 3 4 Example: Each pixel has (Manhattan) distance to nearest edge pixel.

67. 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 11 1 1 1 1 2 2 2 2 2 2 2 2 2 2 223 3 3 3 3 3 3 3 4 Then, try all translations of model edges. Add distances under each edge pixel.

68. Pose: Ransac Match enough features in model to features in image to determine pose. Examples: –match a point and determine translation. –match a corner and determine translation and rotation. –Points and translation, rotation, scaling? –Lines and rotation and translation?

69. Recap: Recognition w/ RANSAC 1.Find features in model and image. –Such as corners. 2.Match enough to determine pose. –Such as 3 points for planar object, scaled orthographic projection. 3.Determine pose. 4.Project rest of object features into image. 5.Look to see how many image features they match. –Example: with bounded error, count how many object features project near an image feature. 6.Repeat steps 2-5 a bunch of times. 7.Pick pose that matches most features.

70. Other Viewpoint Invariants Parallelism Convexity Common region ….