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Erschienen in:
2014 | OriginalPaper | Buchkapitel
Audio-Visual Emotion Analysis Using Semi-Supervised Temporal Clustering with Constraint Propagation
verfasst von : Rodrigo Araujo, Mohamed S. Kamel
Erschienen in: Image Analysis and Recognition
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In this paper, we investigate applying semi-supervised clustering to audio-visual emotion analysis, a complex problem that is traditionally solved using supervised methods. We propose an extension to the semi-supervised aligned cluster analysis algorithm (SSACA), a temporal clustering algorithm that incorporates pairwise constraints in the form of
must-link
and
cannot-link
. We incorporate an exhaustive constraint propagation mechanism to further improve the clustering process. To validate the proposed method, we apply it to emotion analysis on a multimodal naturalistic emotion database. Results show substantial improvements compared to the original aligned clustering analysis algorithm (ACA) and to our previously proposed semi-supervised approach.