1 / 69

How many object categories are there?

How many object categories are there?. ~10,000 to 30,000. Biederman 1987. So what does object recognition involve?. Verification: is that a lamp?. Detection: are there people?. Identification: is that Potala Palace?. Object categorization. mountain. tree. building. banner. street lamp.

kera
Télécharger la présentation

How many object categories are there?

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. How many object categories are there? ~10,000 to 30,000 Biederman 1987

  2. So what does object recognition involve?

  3. Verification: is that a lamp?

  4. Detection: are there people?

  5. Identification: is that Potala Palace?

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

  7. Scene and context categorization • outdoor • city • …

  8. Computational photography

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

  10. Improving online search Query: STREET Organizing photo collections

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

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

  13. Challenges 3: occlusion Magritte, 1957

  14. Challenges 4: scale

  15. Challenges 5: deformation Xu, Beihong 1943

  16. Challenges 6: background clutter Klimt, 1913

  17. Challenges 7: intra-class variation

  18. Issues • RepresentationHow to represent image, memory? • Low or high level? Concrete or abstract? • Appearance, shape, or both? • Local or global? • Invariance, dimensionality reduction • Memory contactWhen do you make it? • Bottom up? Top down? • Difficulty of computation

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

  20. Local, low level Representation The image itself

  21. Local, low level Representation

  22. Representation • Part-based

  23. Representation • Part-based

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

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

  26. Jigsaw approach: Borenstein and Ullman, 2002

  27. Global representation

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

  29. Recognition • Scale / orientation range to search over • Speed • Context

  30. 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.

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

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

  33. convert x into v1, v2 coordinates What does the v2 coordinate measure? • distance to line • use it for classification—near 0 for orange pts What does the v1 coordinate measure? • position along line • use it to specify which orange point it is 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

  34. Dimensionality reduction • How to find v1 and v2 ? • - work out on board • Dimensionality reduction • We can represent the orange points with only their v1 coordinates • since v2 coordinates are all essentially 0 • This makes it much cheaper to store and compare points • A bigger deal for higher dimensional problems

  35. 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: v1 is eigenvector of A with largest eigenvalue v2 is eigenvector of A with smallest eigenvalue

  36. 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

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

  38. 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 v1, v2, ..., vK • any face

  39. Eigenfaces • PCA extracts the eigenvectors of A • Gives a set of vectors v1, v2, v3, ... • Each one of these vectors is a direction in face space • what do these look like?

  40. Projecting onto the eigenfaces • The eigenfaces v1, ..., vK span the space of faces • A face is converted to eigenface coordinates by

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

  42. i = K NM Choosing the dimension K 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

  43. 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?

  44. Skin classifier • Given X = (R,G,B): how to determine if it is skin or not? 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?

  45. 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

  46. Choose interpretation of highest probability • set X to be a skin pixel if and only if Where do we get and ? 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?

  47. Approach: fit parametric PDF functions • common choice is rotated Gaussian • center • covariance • orientation, size defined by eigenvecs, eigenvals 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 Ri contains the proportion of skin pixels with color Ri This doesn’t work as well in higher-dimensional spaces. Why not?

More Related