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

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

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

OUTLINE Introduction Algorithm Experimental & Result Conclusion

Algorithm

Poselet Activations Given a test image Algorithm

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

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

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

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

OUTLINE Introduction Algorithm Experimental & Result Conclusion

Experiment & Result

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

“Has long hair” “Wears glasses”

“Wears shorts” “Has long sleeves”

“Doesn’t have long sleeves”

Experiment & Result

OUTLINE Introduction Algorithm Experimental & Result Conclusion

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

Thank You

oselets/ oselets/ 2/2-3-2.m4v 2/2-3-2.m4v elets/poselets_person.html elets/poselets_person.html