A Novel Framework for Estimating Viewer Interest by Unsupervised Multimodal Anomaly Detection

A reliable method to estimate viewer interest is highly sought after for human-centered video information retrieval. A method that estimates viewer interest while users are watching Web videos is presented in this paper. The method uses a framework for anomaly detection based on collaborative use of facial expression and biological signals such as electroencephalogram (EEG) signals. To the best of our knowledge, there have been no studies that have taken into account two actual mechanisms of the behavior of users while they are watching Web videos. First, whereas most Web videos garner very little attention, a small number attract millions of views. Therefore, a framework for anomaly detection is newly applied to facial expression and EEG in order to model the imbalanced distribution of popularity. Second, since the number of Web videos that are labeled by users as interesting/not interesting is generally too small to estimate viewer interest by a supervised approach, the proposed method utilizes parametric techniques for anomaly detection, which estimates viewer interest in an unsupervised way. Unlike some related studies for estimating viewer interest, our method takes into account actual mechanisms of the behavior of users while they are watching Web videos by utilizing parametric techniques for anomaly detection. Then viewer interest can be estimated on the basis of an anomaly score calculated from our proposed method. Consequently, successful estimation of viewer interest based on a framework for anomaly detection, via collaborative use of facial expression and biological signals, becomes feasible.