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  • Issue No. 7 - July
  • Abstract - Discovering Motion Primitives for Unsupervised Grouping and One-Shot Learning of Human Actions, Gestures, and Expressions
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Discovering Motion Primitives for Unsupervised Grouping and One-Shot Learning of Human Actions, Gestures, and Expressions
July 2013 (vol. 35 no. 7)
pp. 1635-1648
Yang Yang, Dept. of Electr. Eng. & Comput. Sci. (EECS), Univ. of Central Florida (UCF), Orlando, FL, USA
I. Saleemi, Dept. of Electr. Eng. & Comput. Sci. (EECS), Univ. of Central Florida (UCF), Orlando, FL, USA
M. Shah, Dept. of Electr. Eng. & Comput. Sci. (EECS), Univ. of Central Florida (UCF), Orlando, FL, USA
This paper proposes a novel representation of articulated human actions and gestures and facial expressions. The main goals of the proposed approach are: 1) to enable recognition using very few examples, i.e., one or k-shot learning, and 2) meaningful organization of unlabeled datasets by unsupervised clustering. Our proposed representation is obtained by automatically discovering high-level subactions or motion primitives, by hierarchical clustering of observed optical flow in four-dimensional, spatial, and motion flow space. The completely unsupervised proposed method, in contrast to state-of-the-art representations like bag of video words, provides a meaningful representation conducive to visual interpretation and textual labeling. Each primitive action depicts an atomic subaction, like directional motion of limb or torso, and is represented by a mixture of four-dimensional Gaussian distributions. For one--shot and k-shot learning, the sequence of primitive labels discovered in a test video are labeled using KL divergence, and can then be represented as a string and matched against similar strings of training videos. The same sequence can also be collapsed into a histogram of primitives or be used to learn a Hidden Markov model to represent classes. We have performed extensive experiments on recognition by one and k-shot learning as well as unsupervised action clustering on six human actions and gesture datasets, a composite dataset, and a database of facial expressions. These experiments confirm the validity and discriminative nature of the proposed representation.
Index Terms:
Humans,Optical imaging,Spatiotemporal phenomena,Training,Vectors,Joints,Histograms,Hidden Markov model,Human actions,one-shot learning,unsupervised clustering,gestures,facial expressions,action representation,action recognition,motion primitives,motion patterns,histogram of motion primitives,motion primitives strings
Citation:
Yang Yang, I. Saleemi, M. Shah, "Discovering Motion Primitives for Unsupervised Grouping and One-Shot Learning of Human Actions, Gestures, and Expressions," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 7, pp. 1635-1648, July 2013, doi:10.1109/TPAMI.2012.253
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