TY - JOUR
T1 - Learning spatial context from tracking using penalised likelihoods
AU - McKenna, Stephen J.
AU - Nait-Charif, Hammadi
N1 - Copyright 2008 Elsevier B.V., All rights reserved.
PY - 2004/1/1
Y1 - 2004/1/1
N2 - MAP estimation of Gaussian mixtures through maximisation of penalised likelihoods was used to learn models of spatial context. This enabled prior beliefs about the scale, orientation and elongation of semantic regions to be encoded, encouraging one-to-one correspondences between mixture components and these regions. In conjunction with minimum description length this enabled automatic learning of inactivity zones and entry zones from track data in a supportive home environment.
AB - MAP estimation of Gaussian mixtures through maximisation of penalised likelihoods was used to learn models of spatial context. This enabled prior beliefs about the scale, orientation and elongation of semantic regions to be encoded, encouraging one-to-one correspondences between mixture components and these regions. In conjunction with minimum description length this enabled automatic learning of inactivity zones and entry zones from track data in a supportive home environment.
UR - http://www.scopus.com/inward/record.url?scp=10044237579&partnerID=8YFLogxK
U2 - 10.1109/ICPR.2004.1333723
DO - 10.1109/ICPR.2004.1333723
M3 - Article
AN - SCOPUS:10044237579
SN - 1051-4651
VL - 4
SP - 138
EP - 141
JO - Proceedings - International Conference on Pattern Recognition
JF - Proceedings - International Conference on Pattern Recognition
ER -