Implicit hybrid video emotion tagging by integrating video content and users' multiple physiological responses

The intrinsic interactions among a video's emotion tag, its content, and a user's spontaneous response while consuming the video can be leveraged to improve video emotion tagging, but this capability has not been thoroughly exploited yet. In this paper, we propose an implicit hybrid video emotion tagging approach by integrating video content and users' multiple physiological responses, which are only required during training. Specifically, multiple physiological signals during training construct a better emotion tagging model from video content. We add similarity constraints on the classifier mapping functions during training to capture the relationships among different kinds of features. We modify the traditional support vector machine with these constraints to improve video emotion tagging. Efficient learning algorithms of the proposed model are also developed. Experiments on three benchmark databases demonstrate the effectiveness and superior performance of our proposed method for implicitly integrating multiple physiological responses to improve video emotion tagging.