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Visual Object Recognition

Visual Object Recognition. Rob Fergus Courant Institute, New York University. http://cs.nyu.edu/~fergus/icml_tutorial/. Agenda. Introduction Bag-of-words models Visual words with spatial location Part-based models Discriminative methods Segmentation and recognition

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Visual Object Recognition

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  1. Visual Object Recognition Rob Fergus Courant Institute, New York University http://cs.nyu.edu/~fergus/icml_tutorial/

  2. Agenda • Introduction • Bag-of-words models • Visual words with spatial location • Part-based models • Discriminative methods • Segmentation and recognition • Recognition-based image retrieval • Datasets & Conclusions

  3. Recognizing and Learning Object Categories: Year 2007 Li Fei-Fei, Princeton Rob Fergus, NYU Antonio Torralba, MIT http://people.csail.mit.edu/torralba/shortCourseRLOC

  4. Agenda • Introduction • Bag-of-words models • Visual words with spatial location • Part-based models • Discriminative methods • Segmentation and recognition • Recognition-based image retrieval • Datasets & Conclusions

  5. So what does object recognition involve?

  6. Classification: are there street-lights in the image?

  7. Detection: localize the street-lights in the image

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

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

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

  11. Application:Computational photography

  12. Application: Improving online search Query: STREET Organizing photo collections

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

  14. Challenges 2: scale

  15. Challenges 3: illumination slide credit: S. Ullman

  16. Challenges 4: background clutter Bruegel, 1564

  17. Challenges 5: occlusion http://lh5.ggpht.com/_wJc6t2hDl2M/RrL7Gh6sS7I/AAAAAAAAAYY/n3xaHc2opls/DSC00633.JPG

  18. Challenges 6: deformation http://img.timeinc.net/time/asia/magazine/2007/1112/racehorse_1112.jpg Xu, Beihong 1943

  19. History: single object recognition Object 1 Object 2 Object 3

  20. Single object recognition history: Geometric methods David Lowe [1985] Rothwell et al. [1992]

  21. Single object recognition history: Appearance-based methods • Murase & Nayer 1995 • Schmid & Mohr 1997 • Lowe, et al. 1999, 2003 • Mahamud and Herbert, 2000 • Ferrari et al. 2004 • Rothganger et al. 2004 • Moreels and Perona, 2005 • …

  22. Challenges 7: intra-class variation Shoe class Instance 1 Instance 2 Instance 3

  23. History: early object categorization

  24. Fischler, Elschlager, 1973 • Turk and Pentland, 1991 • Belhumeur, Hespanha, & Kriegman, 1997 • Rowley & Kanade, 1998 • Schneiderman & Kanade 2004 • Viola and Jones, 2000 • Heisele et al., 2001 • Amit and Geman, 1999 • LeCun et al. 1998 • Belongie and Malik, 2002 • DeCoste and Scholkopf, 2002 • Simard et al. 2003 • Poggio et al. 1993 • Argawal and Roth, 2002 • Schneiderman & Kanade, 2004 • …..

  25. ~10,000 to 30,000

  26. Three main issues • Representation • How to represent an object category • Learning • How to form the classifier, given training data • Recognition • How the classifier is to be used on novel data

  27. Representation • Generative / discriminative / hybrid

  28. Representation • Generative / discriminative / hybrid • Appearance only or location and appearance

  29. Representation • Generative / discriminative / hybrid • Appearance only or location and appearance • Invariances • View point • Illumination • Occlusion • Scale • Deformation • Clutter • etc.

  30. Representation • Generative / discriminative / hybrid • Appearance only or location and appearance • Invariances • Part-based or global with sub-window

  31. Representation • Generative / discriminative / hybrid • Appearance only or location and appearance • Invariances • Parts or global w/sub-window • Use set of features or each pixel in image

  32. Learning • Unclear how to model categories, so learn rather than manually specify

  33. Learning • Unclear how to model categories, so learn rather than manually specify • Methods of training: generative vs. discriminative

  34. Learning • Unclear how to model categories, so learn rather than manually specify • Methods of training: generative vs. discriminative • Level of supervision • Manual segmentation; bounding box; image labels; noisy labels Contains a motorbike

  35. Learning • Unclear how to model categories, so learn rather than manually specify • Methods of training: generative vs. discriminative • Level of supervision • Manual segmentation; bounding box; image labels; noisy labels • -- Training images: • Issue of over-fitting (typically limited training data) • Negative images for discriminative methods

  36. Learning • Unclear how to model categories, so learn rather than manually specify • Methods of training: generative vs. discriminative • Level of supervision • Manual segmentation; bounding box; image labels; noisy labels • -- Training images: • Issue of over-fitting (typically limited training data) • Negative images for discriminative methods • -- Priors

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

  38. Recognition • Context enables pruning of detector output Hoiem, Efros, Herbert, 2006

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