Research on Summary Highlight Ranking of Sports Video

A novel multimodal approach of highlight ranking for sports video summaries in affective context was proposed based on player behavior information and audio keywords of sports game. The mid-level representation trajectory-action-audio is constructed for the video content by fusing the information of player trajectory, action and audio keywords. Based on trajectory-action-audio, the computational affective features are extracted to describe the objective process of highlight ranking of sports video summaries from user subjective perception. A kernel based nonlinear probabilistic ranking model construction method is proposed, which is robust for the noisy data and provided with good expansibility. In addition, a new subjective evaluation criterion is proposed to guide model construction and feature extraction with the assistance of forward search algorithm.

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