@InProceedings{kobayashi:wcecs:2013,
  author    = {Kobayashi, Mizuki and Kobayashi, Ichiro and Asoh, Hideki and Guadarrama, Sergio},
  title     = {A Probabilistic Approach to Text Generation of Human Motions extracted from Kinect Videos},
  booktitle = {World Congress on Engineering and Computer Science},
  year      = {2013},
  address   = {San Francisco, USA},
  month     = {October 23-October 25},
  url       = {http://www.iaeng.org/publication/WCECS2013/WCECS2013\_pp18-22.pdf},
  keywords  = {probabilistic text generation, bi-gram, Symbolic Aggregation approximation(SAX), time-series data, Kineet},
  abstract  = {In this study, we propose a framework for probabilistic text generation of human motions extracted from Kinect videos. We capture human motions by a Kinect camera and
extract the time-series data of the motions from the videos. The time-series data are applied by several dimension reduction procedures and then turned to be the form which can be applied
to machine learning. A pair of the analyzed time-series data and its intermediate representation which corresponds to the semantics of the human motion is learned by a log-linear model.
As linguistic resources to generate a text, we collected various natural language expressions for human motions and build a bi-gram model for each motion. In our framework, once the
intermediate representation is decided by observing time-series data; a proper bi-gram model corresponding to the intermediate representation is chosen; and then a text is generated by solving dynamic programming of the bi-gram model. Through
experiments to generate texts describing human motions, we have confirmed that our proposed framework works well.}
}