The Detection of Concept Frames Using Clustering Multi-instance Learning

The classification of sequences requires the combination of information from different time points. In this paper the detection of facial expressions is considered. Experiments on the detection of certain facial muscle activations in videos show that it is not always required to model the sequences...

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Main Authors: Tax, D M J, Hendriks, E, Valstar, M F, Pantic, M
Format: Conference Proceeding
Language:English
Subjects:
classification
Data models
Gold
Hidden Markov models
Logistics
multi-instance learning
Pattern recognition
Time series analysis
time series classification
Training
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Hendriks, E
Valstar, M F
Pantic, M
description The classification of sequences requires the combination of information from different time points. In this paper the detection of facial expressions is considered. Experiments on the detection of certain facial muscle activations in videos show that it is not always required to model the sequences fully, but that the presence of specific frames (the concept frame) can be sufficient for a reliable detection of certain facial expression classes. For the detection of these concept frames a standard classifier is often sufficient, although a more advanced clustering approach performs better in some cases.
doi_str_mv 10.1109/ICPR.2010.715
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subjects classification
Data models
Gold
Hidden Markov models
Logistics
multi-instance learning
Pattern recognition
Time series analysis
time series classification
Training
title The Detection of Concept Frames Using Clustering Multi-instance Learning
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