Affective video segment retrieval for consumer generated videos based on correlation between emotions and emotional audio events
A novel affective video segment retrieval method based on the correlation between emotion and emotional audio events (EAEs) is presented. The proposed method focuses on retrieving three types of affective video segments, joy, sadness and excitement, by utilizing correlations between emotions and EAEs. The correlation between these emotions and EAEs is investigated by a subjective evaluation. The proposed method detects EAEs and rates each EAE in terms of emotion levels. The EAEs are detected by using the Generalized State-Space Model (GSSM) and low-level audio features. Experiments conducted on Consumer Generated Videos (CGVs) show that the proposed EAE detection outperforms conventional HMM and GMM based methods in terms of accuracy, the agreement rate of the retrieved affective video segments reaches 73.3%.