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Published in:
2011 | OriginalPaper | Chapter
Multiple Instance Learning for Classification of Human Behavior Observations
Authors : Athanasios Katsamanis, James Gibson, Matthew P. Black, Shrikanth S. Narayanan
Published in: Affective Computing and Intelligent Interaction
Publisher: Springer Berlin Heidelberg
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Analysis of audiovisual human behavior observations is a common practice in behavioral sciences. It is generally carried through by expert annotators who are asked to evaluate several aspects of the observations along various dimensions. This can be a tedious task. We propose that automatic classification of behavioral patterns in this context can be viewed as a multiple instance learning problem. In this paper, we analyze a corpus of married couples interacting about a problem in their relationship. We extract features from both the audio and the transcriptions and apply the Diverse Density-Support Vector Machine framework. Apart from attaining classification on the expert annotations, this framework also allows us to estimate salient regions of the complex interaction.