Video Background Music Recommendation Based on Multi-Level Fusion Features

People resonate more with music when exposed to visual information, and music enhances their perception of video content. Cross-modal recommendation techniques can be used to suggest appropriate background music for a given video. However, there is not a simple correspondence between the different modal data. Therefore, to explore the association between the two modalities of video and music, we propose MFF-VBMR, a video background music recommendation model based on multi-level fusion features. The model uses the cross-modal information of static, dynamic and emotional content of video and music to realize the task of matching and recommending suitable background music for a given video. Experimental results show that the proposed model outperforms other existing models in terms of performance and achieves satisfactory results for video background music recommendation.