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

2. OUTLINE Introduction Algorithm Experimental & Result Conclusion

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

4. Gender recognition with poselets

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

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. Poselet Activations Given a test image Algorithm

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

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

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

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

14. OUTLINE Introduction Algorithm Experimental & Result Conclusion

15. Experiment & Result

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

17. “Has long hair” “Wears glasses”

18. “Wears shorts” “Has long sleeves”

19. “Doesn’t have long sleeves”

20. 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/p oselets/ http://www.eecs.berkeley.edu/~lbourdev/p oselets/ http://www.iccv2011.org/oral_videos/day_ 2/2-3-2.m4v http://www.iccv2011.org/oral_videos/day_ 2/2-3-2.m4v http://www.cs.berkeley.edu/~lbourdev/pos elets/poselets_person.html http://www.cs.berkeley.edu/~lbourdev/pos elets/poselets_person.html