Title :
Max Margin AND/OR Graph learning for parsing the human body
Author :
Zhu, Long Leo ; Chen, Yuanhao ; Lu, Yifei ; Lin, Chenxi ; Yuille, Alan
Author_Institution :
Dept. of Stat., Univ. of California, Los Angeles, CA
Abstract :
We present a novel structure learning method, Max Margin AND/OR graph (MM-AOG), for parsing the human body into parts and recovering their poses. Our method represents the human body and its parts by an AND/OR graph, which is a multi-level mixture of Markov random fields (MRFs). Max-margin learning, which is a generalization of the training algorithm for support vector machines (SVMs), is used to learn the parameters of the AND/OR graph model discriminatively. There are four advantages from this combination of AND/OR graphs and max-margin learning. Firstly, the AND/OR graph allows us to handle enormous articulated poses with a compact graphical model. Secondly, max-margin learning has more discriminative power than the traditional maximum likelihood approach. Thirdly, the parameters of the AND/OR graph model are optimized globally. In particular, the weights of the appearance model for individual nodes and the relative importance of spatial relationships between nodes are learnt simultaneously. Finally, the kernel trick can be used to handle high dimensional features and to enable complex similarity measure of shapes. We perform comparison experiments on the base ball datasets, showing significant improvements over state of the art methods.
Keywords :
Markov processes; graph theory; learning (artificial intelligence); pose estimation; support vector machines; Markov random fields; compact graphical model; human body parsing; max margin AND/OR graph learning; multi-level mixture; support vector machines; Biological system modeling; Computer vision; Humans; Kernel; Machine learning; Maximum likelihood estimation; Performance analysis; Shape measurement; Statistics; Tree graphs;
Conference_Titel :
Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on
Conference_Location :
Anchorage, AK
Print_ISBN :
978-1-4244-2242-5
Electronic_ISBN :
1063-6919
DOI :
10.1109/CVPR.2008.4587787