Athanasios Katsamanis, James Gibson, Matthew P. Black, and Shrikanth S. Narayanan. Multiple Instance Learning for Classification of Human Behavior Observations. In Proceedings of Affective Computing and Intelligent Interaction (ACII), Lecture Notes in Computer Science, oct 2011.
Analysis of audiovisual human behavior observations is acommon practice in behavioral sciences. It is generally carried throughby expert annotators who are asked to evaluate several aspects of theobservations along various dimensions. This can be a tedious task. Wepropose that automatic classification of behavioral patterns in this contextcan be viewed as a multiple instance learning problem. In this paper, we analyze a corpus of married couples interacting about a problemin their relationship. We extract features from both the audio and thetranscriptions and apply the Diverse Density-Support Vector Machineframework. Apart from attaining classification on the expert annotations, this framework also allows us to estimate salient regions of thecomplex interaction.
@inproceedings{Katsamanis2011MultipleInstanceLearningfor,
abstract = {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.},
author = {Katsamanis, Athanasios and Gibson, James and Black, Matthew P. and Narayanan, Shrikanth S.},
bib2html_rescat = {},
booktitle = {Proceedings of Affective Computing and Intelligent Interaction (ACII), Lecture Notes in Computer Science},
doi = {10.1007/978-3-642-24600-5_18},
link = {http://sail.usc.edu/publications/files/KatsamanisGibsonBlackNarayanan_MIL_HumanBehavior_ACII2011.pdf},
location = {Memphis, TN},
month = {oct},
title = {Multiple Instance Learning for Classification of Human Behavior Observations},
year = {2011}
}
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