Max-margin hidden conditional random fields for human action recognition

We present a new method for classification with structured latent variables. Our model is formulated using the max-margin formalism in the discriminative learning literature. We propose an efficient learning algorithm based on the cutting plane method and decomposed dual optimization. We apply our m...

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Bibliographic Details
Published in2009 IEEE Conference on Computer Vision and Pattern Recognition pp. 872 - 879
Main Authors Yang Wang, Mori, Greg
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2009
Subjects
Computer vision
Humans
Labeling
Object recognition
Optimization methods
Pixel
Torso
Training data
Video sequences
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Summary:We present a new method for classification with structured latent variables. Our model is formulated using the max-margin formalism in the discriminative learning literature. We propose an efficient learning algorithm based on the cutting plane method and decomposed dual optimization. We apply our model to the problem of recognizing human actions from video sequences, where we model a human action as a global root template and a constellation of several "parts". We show that our model outperforms another similar method that uses hidden conditional random fields, and is comparable to other state-of-the-art approaches. More importantly, our proposed work is quite general and can potentially be applied in a wide variety of vision problems that involve various complex, interdependent latent structures.
ISBN:1424439922
9781424439928
ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2009.5206709