Emotional event detection using relevance feedback
Hang-Bong Kang · 2004
Affective content analysis is necessary to represent a user's preferences in various applications such as video data retrieval and video abstraction. In this paper, we propose a new method to detect emotional events such as fear, sadness, and joy from video data using relevance feedback scheme. We ask the user to provide feedbacks regarding the relevance with emotions for video shot. Then, the system is learned from training data to achieve an improved performance in detecting emotional events. Even though simple low level features are used, experimental results are encouraging.