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Unsupervised Salience Learning for Person Re-identification

Unsupervised Salience Learning for Person Re-identification. CVPR2013 Poster. Outline. Introduction Method Experiments Conclusions. Introduction. Human eyes can recognize person identities based on some small salient regions. Introduction.

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Unsupervised Salience Learning for Person Re-identification

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  1. Unsupervised Salience Learning for Person Re-identification CVPR2013 Poster

  2. Outline • Introduction • Method • Experiments • Conclusions

  3. Introduction • Human eyes can recognize person identities based on some small salient regions.

  4. Introduction • Person re-identification handles pedestrian matching and ranking across non-overlapping camera views. • viewpoint change and pose variation cause uncontrolled misalignment between images.

  5. Introduction • Motivations : • 1. We can recognize persons across camera views from their local distinctive regions • 2. Human salience • 3. Distinct patches are considered as salient only when they are matched and distinct in both camera views

  6. Method • Dense Correspondence • Unsupervised Salience Learning • Matching for Re-identification

  7. Dense Correspondence • Features: • dense color histogram + dense SIFT • Adjacency constrained search: • simple patch matching

  8. Adjacency constrained search Search set :

  9. Adjacency constrained search Adjacency Searching:

  10. Unsupervised Salience Learning • two methods for learning human salience: • K-Nearest Neighbor Salience (KNN) • One-Class SVM Salience (OCSVM)

  11. Unsupervised Salience Learning • Definition: Salient regions are discriminative in making a person standing out from their companions, and reliable in finding the same person across camera views. • Assumption: fewer than half of the persons in a reference set share similar appearance if a region is salient. Hence, we set k = Nr/2. Nr is the number of images in reference set.

  12. Unsupervised Salience Learning

  13. K-Nearest Neighbor Salience

  14. K-Nearest Neighbor Salience

  15. Unsupervised Salience Learning

  16. One-Class SVM Salience

  17. Matching for Re-identification • Bi-directional Weighted Matching • Complementary Comination

  18. Matching for Re-identification

  19. Experiments • Dataset : • VIPeR Dataset • ETHZ Dataset

  20. Experiments

  21. Experiments

  22. Experiments

  23. Experiments

  24. Experiments

  25. Conclusions • 1. An unsupervised framework to extract distinctive features for person re-identification. • 2. Patch matching is utilized with adjacency constraint for handling the misalignment problem caused by viewpoint change and pose variation. • 3. Human salience is learned in an unsupervised way.

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