Date post: | 01-Apr-2015 |
Category: |
Documents |
Upload: | tristan-aldis |
View: | 214 times |
Download: | 0 times |
Human Action Recognition by Learning Bases of Action Attributes
and Parts
Bangpeng Yao, Xiaoye Jiang, Aditya Khosla, Andy Lai Lin, Leonidas Guibas, and Li Fei-Fei
Stanford University
Outline
• Introduction• Action Bases• Learning the Dual-Sparse Action Bases and
Reconstruction Coefficients• Experiments
Introduction
• Human action recognition in still images• A general image classification problem• Human-object interaction• Parts + Attributes
• Contributions• Represent each image by using a sparse set of
action bases that are meaningful to the content of the image
• Effectively learn these bases given far-from-perfect detections of action attributes and parts without meticulous human labeling
Action Bases
• Attributes and parts• Attributes: verb, learned
by discriminative classifiers
• Parts: object parts and poselets, learned by pre-trained object detectors and poselet detectors
• A vector of the normalized confidence scores obtained from these classifiers and detectors is used to represent this image.
Action Bases
• High-order interactions of image attributes and parts
• is used to represent each image and SVMs are trained for action classification
Dual-sparsity Learning
Experiments
• PASCAL actions• Stanford 40 actions
• PASCAL
• Stanford 40 actions