Global Affective Video Content Regression Based on Complementary Audio-Visual Features

In this paper, we propose a new framework for global affective video content regression with five complementary audio-visual features. For the audio modality, we select the global audio feature eGeMAPS and two deep features SoundNet and VGGish. As for the visual modality, the key frames of original images and those of optical flow images are both used to extract VGG-19 features with finetuned models, in order to represent the original visual cues in conjunction with motion information. In the experiments, we perform the evaluations of selected audio and visual features on the dataset of Emotional Impact of Movies Task 2016 (EIMT16), and compare our results with those of competitive teams in EIMT16 and state-of-the-art method. The experimental results show that the fusion of five features can achieve better regression results in both arousal and valence dimensions, indicating the selected five features are complementary with each other in the audio-visual modalities. Furthermore, the proposed approach can achieve better regression results than the state-of-the-art method in both evaluation metrics of MSE and PCC in the arousal dimension and comparable MSE results in the valence dimension. Although our approach obtains slightly lower PCC result than the state-of-the-art method in the valence dimension, the fused feature vectors used in our framework have much lower dimensions with a total of 1752, only five thousandths of feature dimensions in the state-of-the-art method, largely bringing down the memory requirements and computational burden.