Multiple instance learning for classification of human behavior observations

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. © 2011 Springer-Verlag.