Auto clustering for unsupervised learning of atomic gesture components using minimum description length

@inproceedings{citeulike:2288402, abstract = {We present an approach to automatically segment and label a continuous observation sequence of hand gestures for a complete unsupervised model acquisition. The method is based on the assumption that gestures can be viewed as repetitive sequences of atomic components, similar to phonemes in speech, governed by a high level structure controlling the temporal sequence. We show that the generating process for the atomic components can be described in gesture space by a mixture of Gaussian, with each mixture component tied to one atomic behaviour. Mixture components are determined using a standard expectation maximisation approach while the determination of the number of components is based on an information criteria, the minimum description length}, author = {Walter, M. and Psarrou, A. and Gong, S. }, booktitle = {Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems, 2001. Proceedings. IEEE ICCV Workshop on}, citeulike-article-id = {2288402}, doi = {http://dx.doi.org/10.1109/RATFG.2001.938925}, journal = {Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems, 2001. Proceedings. IEEE ICCV Workshop on}, keywords = {gesture, recognition}, pages = {157--162}, posted-at = {2008-01-25 07:49:45}, priority = {5}, title = {Auto clustering for unsupervised learning of atomic gesture components using minimum description length}, url = {http://dx.doi.org/10.1109/RATFG.2001.938925}, year = {2001} }

TY - CONF ID - citeulike:2288402 L3 - citeulike-article-id:2288402 TI - Auto clustering for unsupervised learning of atomic gesture components using minimum description length T2 - Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems, 2001. Proceedings. IEEE ICCV Workshop on JF - Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems, 2001. Proceedings. IEEE ICCV Workshop on SP - 157 EP - 162 N2 - We present an approach to automatically segment and label a continuous observation sequence of hand gestures for a complete unsupervised model acquisition. The method is based on the assumption that gestures can be viewed as repetitive sequences of atomic components, similar to phonemes in speech, governed by a high level structure controlling the temporal sequence. We show that the generating process for the atomic components can be described in gesture space by a mixture of Gaussian, with each mixture component tied to one atomic behaviour. Mixture components are determined using a standard expectation maximisation approach while the determination of the number of components is based on an information criteria, the minimum description length KW - gesture KW - recognition AU - Walter, M AU - Psarrou, A AU - Gong, S PY - 2001/// UR - http://dx.doi.org/10.1109/RATFG.2001.938925 ER -