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