A robust approach towards sequential data modeling and its application in automatic gesture recognition

Chatzis, S; Kosmopoulos, DI; Varvarigou, T

dc.contributor.author	Chatzis, S	en
dc.contributor.author	Kosmopoulos, DI	en
dc.contributor.author	Varvarigou, T	en
dc.date.accessioned	2014-03-01T02:45:04Z
dc.date.available	2014-03-01T02:45:04Z
dc.date.issued	2008	en
dc.identifier.issn	15206149	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/32124
dc.subject	Pattern classification	en
dc.subject	Pattern clustering methods	en
dc.subject	Pattern recognition	en
dc.subject.other	Acoustics	en
dc.subject.other	Blind source separation	en
dc.subject.other	Communication channels (information theory)	en
dc.subject.other	Gesture recognition	en
dc.subject.other	Image segmentation	en
dc.subject.other	Markov processes	en
dc.subject.other	Mathematical models	en
dc.subject.other	Maximum likelihood estimation	en
dc.subject.other	Mixtures	en
dc.subject.other	Object recognition	en
dc.subject.other	Parameter estimation	en
dc.subject.other	Signal processing	en
dc.subject.other	Speech	en
dc.subject.other	Students	en
dc.subject.other	Time series analysis	en
dc.subject.other	Trees (mathematics)	en
dc.subject.other	Trellis codes	en
dc.subject.other	Pattern classification	en
dc.subject.other	Pattern clustering methods	en
dc.subject.other	Pattern recognition	en
dc.subject.other	State distributions	en
dc.subject.other	Hidden Markov models	en
dc.title	A robust approach towards sequential data modeling and its application in automatic gesture recognition	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/ICASSP.2008.4518015	en
heal.identifier.secondary	http://dx.doi.org/10.1109/ICASSP.2008.4518015	en
heal.identifier.secondary	4518015	en
heal.publicationDate	2008	en
heal.abstract	Hidden Markov models using finite Gaussian mixture models as their hidden state distributions have been applied in modeling of time series that result from various noisy signals. Nevertheless, Gaussian mixture models are well-known to be highly intolerant to the presence of outliers within the fitting sets used for their estimation. Finite Student's-t mixture models have recently emerged as a heaviertailed, robust alternative to Gaussian mixture models, overcoming these hurdles. To exploit those merits of Student's-t mixture models, we introduce in this paper a novel hidden Markov chain model where the hidden state distributions are considered to be finite mixtures of multivariate Student's-t densities and we derive an algorithm for the model parameters estimation under a maximum likelihood framework. We apply this novel approach in automatic gesture recognition and we show that our model provides a substantial improvement in data representation performance and computational efficiency over the standard Gaussian model. ©2008 IEEE.	en
heal.journalName	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings	en
dc.identifier.doi	10.1109/ICASSP.2008.4518015	en
dc.identifier.spage	1937	en
dc.identifier.epage	1940	en