A Multiple Visual Models Based Perceptive Analysis Framework for Multilevel Video Summarization

In this paper, we propose a generic framework to human perception analysis in video understanding based on multiple visual cues. Video features that prominently influence human perception, such as motion, contrast, special scenes, and statistical rhythm, are first extracted and modeled. A perception curve that corresponds to human perception change is then constructed from these individual models using linear or priority based fusion approach. As an important application of the perceptive analysis framework, a feasible scheme for video summarization is implemented in order to demonstrate the validity, robustness, and generality of the proposed framework. The frames that correspond to the peak points in these individual models and the fusion curve are extracted as multilevel summarizations that include video keywords, keyframes, and dynamic segments. The subjective evaluations from a supplementary volunteer study on video summarizations indicate that the analysis framework is effective and offer a promising approach to semantic video management, access, and understanding