1 / 42

The Visual Recognition Machine

The Visual Recognition Machine. Jitendra Malik University of California at Berkeley. From images to objects. Labeled sets: tiger, grass etc. Possible for both instances or object classes (Mona Lisa vs. faces or Beetle vs. cars). Tolerant to changes in pose and illumination. Recognition.

rogersjerry
Télécharger la présentation

The Visual Recognition Machine

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. The Visual Recognition Machine Jitendra Malik University of California at Berkeley

  2. From images to objects Labeled sets: tiger, grass etc

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

  4. Three stages • Segmentation: Images Regions • Association: Regions Super-regions • Matching: Super-regions Prototype views

  5. Three stages • Segmentation: Images Regions • Association: Regions Super-regions • Matching: Super-regions Prototype views

  6. Boundaries of image regions defined by a number of attributes • Brightness/color • Texture • Motion • Stereoscopic depth • Familiar configuration

  7. Image Segmentation as Graph Partitioning Build a weighted graph G=(V,E) from image V: image pixels E: connections between pairs of nearby pixels Partition graph so that similarity within group is large and similarity between groups is small -- Normalized Cuts [Shi&Malik 97]

  8. Some Terminology for Graph Partitioning • How do we bipartition a graph:

  9. Normalized Cut, A measure of dissimilarity • Minimum cut is not appropriate since it favors cutting small pieces. • Normalized Cut, Ncut:

  10. Solving the Normalized Cut problem • Exact discrete solution to Ncut is NP-complete even on regular grid, • [Papadimitriou’97] • Drawing on spectral graph theory, good approximation can be obtained by solving a generalized eigenvalue problem.

  11. Normalized Cut As Generalized Eigenvalue problem • after simplification, we get

  12. Computational Aspects • Solving for the generalized eigensystem: • (D-W) is of size , but it is sparse with O(N) nonzero entries, where N is the number of pixels. • Using Lanczos algorithm.

  13. Three stages • Segmentation: Images Regions • Association: Regions Super-regions • Matching: Super-regions Prototype views

  14. Association • Number of super-regions of size k in image with n regions is approximately (4**k)*n/k • For typical images, this ranges between 1000 and 10000 • Plausibility ordering could reduce effective number substantially • Computing time for this stage negligible

  15. Three stages • Segmentation: Images Regions • Association: Regions Super-regions • Matching: Super-regions Prototype views

  16. Matching • Objects are represented by a set of prototypical views (~10 per object) • For each super-region S, calculate probability that it is an instance of view V • Determine most probable labeling of image into objects

  17. Matching super-regions to views • Based on color, texture and shape similarity • Color, texture matching is relatively well understood and fast • Shape matching is difficult because the algorithm should tolerate pose, illumination and intra-category variation • GOAL: small misclassification error with few views.

  18. Core idea • Find corresponding points on the two shapes and use those to deform prototype into alignment • Allowing this flexibility reduces number of prototype views needed

  19. MNIST Handwritten Digits

  20. Digit Prototypes

  21. Matching with original and deformed prototypes Prototype Test Error

  22. Deforming prototypes using thin plate splines

  23. Only 25 deformable templates needed (instead of 60 K) to get 5% error

  24. COIL Object Database

  25. Computing cost on a Pentium PC • Segmentation: 5 minutes /image • Matching : 0.5 sec / match

  26. Cost on 10**4 node machine • Segmentation: 0.03 sec /image, which is 30 Hz (video rate) • Matching : 20K matches/sec at full resolution (100 points/shape)

  27. How many prototype views can one match at 1 Hz? • 1K candidate super-regions • Consider only 1% of matches at full resolution (10% pass color/texture filter, 10% of those pass low resolution shape filter) • If half time spent in pruning and half in full resolution matching, 1000 prototype views can be matched at 1 Hz.

  28. What can one do with matching 1000 views a second? • Worst case: 100 object categories • Best case depends on how well one can exploit context, hierarchy and hashing. • Cf. humans can recognize 10-100K objects

  29. Memory requirements • 10 K object categories * 10 views/category * 100 * 100 pixels/view * 1 byte/pixel gives us 1 Gigabyte.

  30. Concluding remarks • Speech in 1985 was in the same state as vision in 2000. Hidden Markov Models adoption led to a decade of research which refined the paradigm for continuous speech recognition. • The proposed 3 stage framework for recognition: segmentation, association and matching, could provide the same focus and coherence to vision research leading to general purpose object recognition in 10 years.

More Related