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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Margolin, G., Oliver, P.H., Gordis, E.B., O’Hearn, H.G., Medina, A.M., Ghosh, C.M., Morland, L.: The nuts and bolts of behavioral observation of marital and family interaction. Clinical Child and Family Psychology Review 1(4), 195–213 (1998)
Jones, J., Christensen, A.: Couples interaction study: Social support interaction rating system. University of California, Los Angeles (1998)
Black, M.P., Katsamanis, A., Lee, C.-C., Lammert, A.C., Baucom, B.R., Christensen, A., Georgiou, P.G., Narayanan, S.: Automatic classification of married couples’ behavior using audio features. In: Proc. Int’l. Conf. on Speech Communication and Technology (2010)
Lee, C.-C., Black, M.P., Katsamanis, A., Lammert, A.C., Baucom, B.R., Christensen, A., Georgiou, P.G., Narayanan, S.: Quantification of prosodic entrainment in affective spontaneous spoken interactions of married couples. In: Proc. Int’l. Conf. on Speech Communication and Technology (2010)
Maron, O., Lozano-Pérez, T.: A framework for multiple instance learning. In: Proc. Advances in Neural Information Processing Systems (1998)
Viola, P., Platt, J.C., Zhang, C.: Multiple instance boosting for object detection. In: Proc. Advances in Neural Information Processing Systems (2006)
Andrews, S., Tsochantaridis, I., Hofmann, T.: Support vector machines for multiple instance learning. In: Proc. Advances in Neural Information Processing Systems (2003)
Chen, Y., Wang, J.Z.: Image categorization by learning and reasoning with regions. Journal of Machine Learning Research 5, 913–939 (2004)
Chen, Y., Bi, J., Wang, J.Z.: MILES: Multiple instance learning via embedded instance selection. IEEE Trans. Pattern Anal. Mach. Intell. 28, 1931–1947 (2006)
Lee, K., Ellis, D.P.W., Loui, A.C.: Detecting local semantic concepts in environmental sounds using Markov model based clustering. In: Proc. IEEE Int’l Conf. Acous., Speech, and Signal Processing (2010)
Satkin, S., Hebert, M.: Modeling the temporal extent of actions. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part I. LNCS, vol. 6311, pp. 536–548. Springer, Heidelberg (2010)
Christensen, A., Atkins, D.C., Berns, S., Wheeler, J., Baucom, D.H., Simpson, L.E.: Traditional versus integrative behavioral couple therapy for significantly and chronically distressed married couples. Journal of Consulting and Clinical Psychology 72(2), 176–191 (2004)
Heavey, C., Gill, D., Christensen, A.: Couples interaction rating system 2 (CIRS2). University of California, Los Angeles (2002)
Katsamanis, A., Black, M., Georgiou, P., Goldstein, L., Narayanan, S.: SailAlign: Robust long speech-text alignment. In: Workshop on New Tools and Methods for Very-Large Scale Phonetics Research (2011)
Sebastiani, F.: Machine learning in automated text categorization. ACM Computing Surveys 34, 1–47 (2002)
Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Information Processing and Management 24, 513–523 (1988)
Yang, Y., Pedersen, J.O.: A comparative study on feature selection in text categorization. In: Proc. Int’l. Conf. on Machine Learning (1997)
Forman, G.: An extensive empirical study of feature selection metrics for text classification. Journal of Machine Learning Research 3, 1289–1305 (2003)
Gabrilovich, E., Markovitch, S.: Text categorization with many redundant features: using aggressive feature selection to make SVMs competitive with C4.5. In: Proc. Int’l. Conf. on Machine Learning (2004)
Ghosh, P.K., Tsiartas, A., Narayanan, S.S.: Robust voice activity detection using long-term signal variability. IEEE Trans. Audio, Speech, and Language Processing (2010) (accepted)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Katsamanis, A., Gibson, J., Black, M.P., Narayanan, S.S. (2011). Multiple Instance Learning for Classification of Human Behavior Observations. In: D’Mello, S., Graesser, A., Schuller, B., Martin, JC. (eds) Affective Computing and Intelligent Interaction. ACII 2011. Lecture Notes in Computer Science, vol 6974. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24600-5_18
Download citation
DOI: https://doi.org/10.1007/978-3-642-24600-5_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24599-2
Online ISBN: 978-3-642-24600-5
eBook Packages: Computer ScienceComputer Science (R0)