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Recognizing Objects and Actions in Images Jitendra Malik U.C. Berkeley

Recognizing Objects and Actions in Images Jitendra Malik U.C. Berkeley. Many kinds of images…. Ordinary optical images/video Ubiquitous, cheap X Ray tomography Volumetric data Range sensors 2.5 D data ……. From images/video to objects. Labeled sets: tiger, grass etc.

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Recognizing Objects and Actions in Images Jitendra Malik U.C. Berkeley

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  1. Recognizing Objects and Actions in ImagesJitendra MalikU.C. Berkeley

  2. Many kinds of images… • Ordinary optical images/video • Ubiquitous, cheap • X Ray tomography • Volumetric data • Range sensors • 2.5 D data • ……

  3. From images/video to objects Labeled sets: tiger, grass etc

  4. Possible for both instances or object classes (Mona Lisa vs. faces or Beetle vs. cars) • Tolerant to changes in pose and illumination, and occlusion Recognition

  5. Examples of Actions • Movement and posture change • run, walk, crawl, jump, hop, swim, skate, sit, stand, kneel, lie, dance (various), … • Object manipulation • pick, carry, hold, lift, throw, catch, push, pull, write, type, touch, hit, press, stroke, shake, stir, turn, eat, drink, cut, stab, kick, point, drive, bike, insert, extract, juggle, play musical instrument (various)… • Conversational gesture • point, … • Sign Language

  6. Outline • Finding/recognizing faces • Recognizing objects • Recognizing actions

  7. Face Detection Carnegie Mellon University Results on various images submitted to the CMU on-line face detector

  8. Accuracy of Face Detection Carnegie Mellon University • MIT-CMU test set • 94% detection rate with false detection every 2 images • Ordinary consumer photographs (according to Gretag Imaging) • 88% detection rate with false detection every image H. Schneiderman and T. Kanade. “Object Detection Using the Statistics of Parts.” To appear in Int. Jour. of Comp. Vision, 2002.

  9. (courtesy, J. Phillips) • Test of commercial-off-the-shelf (COTS) facial recognition products • Test developed by NIST based on FERET - 13,872 x 13,872 image evaluation matrix resulting in over 192 million matches • Formal test was conducted May to June 2000 • Results released in February 2001 • Sponsored by DoD Counterdrug Technology Development Program Office, National Institute of Justice, NAVSEA Crane Division and DARPA http://www.dodcounterdrug.com/facialrecognition/ DoD Counterdrug

  10. Pose Gallery 200 people Probe set 400 images Probability of ID 10° 20° 25° 45°

  11. Illumination Gallery 227 Probability of ID Indoor/Ambient 236 Outdoor 190

  12. FRVT 2000 Results Critical Parameters • Rotations • Distance/resolution • Duplicates • Illumination

  13. Outline • Finding/recognizing faces • Recognizing objects • Recognizing actions

  14. Biological Shape • D’Arcy Thompson: On Growth and Form, 1917 • studied transformations between shapes of organisms

  15. Matching FrameworkBelongie, Malik & Puzicha, PAMI 2002 ... model target • Find correspondences between points on shape • Estimate transformation & measure similarity

  16. Comparing Pointsets

  17. Comparing Shape Contexts Compute matching costs using Chi Squared distance: Recover correspondences by solving linear assignment problem with costs Cij [Jonker & Volgenant 1987]

  18. Matching Framework ... model target • Find correspondences between points on shape • Estimate transformation & measure similarity

  19. Thin Plate Spline Model • 2D counterpart to cubic spline: • Minimizes bending energy: • Solve by inverting linear system • Can be regularized when data is inexact Duchon (1977), Meinguet (1979), Wahba (1991)

  20. MatchingExample model target

  21. Object Recognition Experiments • Handwritten digits • COIL 3D objects (Nayar-Murase) • Human body configurations • Trademarks

  22. Terms in Similarity Score • Shape Context difference • Local Image appearance difference • orientation • gray-level correlation in Gaussian window • … (many more possible) • Bending energy

  23. Handwritten Digit Recognition • MNIST 600 000 (distortions): • LeNet 5: 0.8% • SVM: 0.8% • Boosted LeNet 4: 0.7% • MNIST 60 000: • linear: 12.0% • 40 PCA+ quad: 3.3% • 1000 RBF +linear: 3.6% • K-NN: 5% • K-NN (deskewed): 2.4% • K-NN (tangent dist.): 1.1% • SVM: 1.1% • LeNet 5: 0.95% • MNIST 20 000: • K-NN, Shape Context matching: 0.63%

  24. COIL Object Database

  25. Prototypes Selected for 2 Categories Details in Belongie, Malik & Puzicha (NIPS2000)

  26. Error vs. Number of Views

  27. Human body configurations

  28. Deformable Matching(Mori & Malik, ECCV 2002) • Kinematic chain-based deformation model • Use iterations of correspondence and deformation • Keypoints on exemplars are deformed to locations on query image

  29. Results

  30. Tracking by Repeated Finding

  31. Outline • Finding/recognizing faces • Recognizing objects • Recognizing actions

  32. Examples of Actions • Movement and posture change • run, walk, crawl, jump, hop, swim, skate, sit, stand, kneel, lie, dance (various), … • Object manipulation • pick, carry, hold, lift, throw, catch, push, pull, write, type, touch, hit, press, stroke, shake, stir, turn, eat, drink, cut, stab, kick, point, drive, bike, insert, extract, juggle, play musical instrument (various)… • Conversational gesture • point, … • Sign Language

  33. Activities and Situation Assessment • Example: Withdrawing money from an ATM • Activities constructed by composing actions. Partial order plans may be a good model. • Activities may involve multiple agents • Detecting unusual situations or activity patterns is facilitated by the video  activity transform

  34. Segment/Region-of-interest Features (points, curves, wavelet coefficients..) Correspondence and deform into alignment Recover parameters of generative model Discriminative classifier Segment/volume-of-interest Features (points, curves, wavelets, motion vectors..) Correspondence and deform into alignment Recover parameters of generative model Discriminative classifier Objects in space Actions in spacetime

  35. Key cues for action recognition • “Morpho-kinesics” of action (shape and movement of the body) • Identity of the object/s • Activity context

  36. Image/Video  Stick figure  Action • Stick figures can be specified in a variety of ways or at various resolutions (deg of freedom) • 2D joint positions • 3D joint positions • Joint angles • Complete representation • Evidence that it is effectively computable

  37. Mathematical Challenges • Modeling shape variation • Nearest neighbor search in high dimensions • Combining statistical optimality with computational efficiency • Reconstruction algorithms for novel sensing modalities

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