Affective Video Content Analysis Based on Two Compact AudioVisual Features In this paper, we propose a new framework for affective video content analysis by using two compact audiovisual features. In the proposed framework, the eGeMAPS is first calculated as global audio feature and then the key frames of optical flow images are fed to VGG19 network for implementing the transfer learning and visual feature extraction. Finally for model learning, the logistic regression is employed for affective video content classification. In the experiments, we perform the evaluations of audio and visual features on the dataset of Affective Impact of Movies Task 2015 (AIMT15), and compare our results with those of competition teams participated in AIMT15. The comparison results show that the proposed framework can achieve the comparable classification result with the first place of AIMT15 with a total feature dimension of 344, which is only about one thousandth of feature dimensions used in the first place of AIMT15.

Recognition of Emotion According to the Physical Elements of the Video. The increasing interest in the effects of emotion on cognitive, social, and neural processes creates a constant need for efficient and reliable techniques for emotion elicitation. Emotions are important in many areas, especially in advertising design and video production. The impact of emotions on the audience plays an important role. This paper analyzes the physical elements in a twodimensional emotion map by extracting the physical elements of a video (color, light intensity, sound, etc.). We used knearest neighbors (KNN), support vector machine (SVM), and multilayer perceptron (MLP) classifiers in the machine learning method to accurately predict the four dimensions that express emotions, as well as summarize the relationship between the twodimensional emotion space and physical elements when designing and producing video.