Audio-Visual Emotion Analysis Using Semi-Supervised Temporal Clustering with Constraint Propagation View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2014

AUTHORS

Rodrigo Araujo , Mohamed S. Kamel

ABSTRACT

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. More... »

PAGES

3-11

References to SciGraph publications

Book

TITLE

Image Analysis and Recognition

ISBN

978-3-319-11754-6
978-3-319-11755-3

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-11755-3_1

DOI

http://dx.doi.org/10.1007/978-3-319-11755-3_1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1011987165


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