Data Driven Model Acquisition using Minimum Description Length

M Walter, A Psarrou and S Gong

An approach is presented 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, starting and ending in a rest position and governed by a high level structure controlling the temporal sequence. It is shown that the generating processes for the atomic components and derived gesture models can be described by a mixture of Gaussian in their respective component and gesture space. Mixture components modelling atomic components and gestures respectively are determined using a standard EM approach, while the determination of the number of mixture components and therefore the number of atomic components and gestures is based on an information criterion, the Minimum Description Length (MDL). Keywords: Gesture Recognition, Data Driven Model Acquisition, Automatic Segmentation, Automatic Labelling, Model Order Selection, Minimum Description Length (MDL), Atomic Gesture Components, Unsupervised Learning.

PDF version

Home Contents Author index Keyword index

Valid CSS! Valid HTML 4.01!

This document produced for BMVC 2001