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Describing People: A Poselet -Based Approach to Attribute Classification

Describing People: A Poselet -Based Approach to Attribute Classification. OUTLINE. Introduction Algorithm Experimental & Result Conclusion. Who has long hair?. [ Bourdev et al., ICCV11]. Gender recognition with poselets. Gender recognition is easier if we factor out the pose.

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Describing People: A Poselet -Based Approach to Attribute Classification

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  1. Describing People: A Poselet-Based Approach to Attribute Classification

  2. OUTLINE • Introduction • Algorithm • Experimental & Result • Conclusion

  3. Who has long hair? [Bourdevet al., ICCV11]

  4. Gender recognition with poselets

  5. Gender recognition is easier if we factor out the pose [Bourdevet al., ICCV11]

  6. Introduction • Dataset: 8035 images • H3D dataset • PASCAL VOC 2010 • 4013 training, 4022 test images • Use Amazon Mechanical Turk to label

  7. OUTLINE • Introduction • Algorithm • Experimental & Result • Conclusion

  8. Algorithm

  9. Algorithm • Given a test image Poselet Activations

  10. Features Poselet patch Skin mask Arms mask B .* C Features Poselet Activations

  11. Poselet-Level Classification • Poselet-level attribute classifiers Poselet-level Classifiers Features Poselet Activations

  12. Person-Level Classification Person-level Classifiers Poselet-level Classifiers Features Poselet Activations

  13. Context-Level Classification Use an SVM with quadratic kernel Context-level Classifiers Person-level Classifiers Poselet-level Classifiers Features Poselet Activations

  14. OUTLINE • Introduction • Algorithm • Experimental & Result • Conclusion

  15. Experiment & Result

  16. Visual search on our test set “Wears hat” “Female”

  17. “Has long hair” “Wears glasses”

  18. “Wears shorts” “Has long sleeves”

  19. “Doesn’t have long sleeves”

  20. Experiment & Result

  21. Experiment & Result

  22. Experiment & Result

  23. OUTLINE • Introduction • Algorithm • Experimental & Result • Conclusion

  24. Conclusion • Three layer feed-forward network • A large dataset • 8035 people annotated with 9 attributes • A poselet-based approach • Simple and effective

  25. Thank You

  26. http://www.eecs.berkeley.edu/~lbourdev/poselets/ • http://www.iccv2011.org/oral_videos/day_2/2-3-2.m4v • http://www.cs.berkeley.edu/~lbourdev/poselets/poselets_person.html

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