Learning-Based Movie Summarization via Role-Community Analysis and Feature Fusion

Movie summarization aims at condensing a full-length movie to a significantly shortened version that still preserves the movie's major semantic content. In this paper, we propose a learning-based movie summarization framework via role-community social network analysis and feature fusion. In our framework, scene-based movie summarization is formulated as a 0-1 knapsack problem, where the scene attention value for each significant scene is calculated as its "value" and the length of this scene is used as its "cost." To identify the significance of each scene, we propose a learning-based approach to fuse the information derived from visual saliency (based on low-level features and high-level cognitive process for an input movie), high-level semantic analysis (based on the global and local social networks constructed from the movie), and user preferences. Our evaluation results show that in most test cases, the proposed method subjectively outperforms attention-based and role-based summarization methods and our previous role-community-based method in terms of semantic content preservation.