Highlight Ranking for Racquet Sports Video in User Attention Subspaces Based on Relevance Feedback
In this paper, we propose a method to rank the highlights of broadcast racquet sports videos. Compared with previous work, we integrate relevance feedback into highlight ranking framework to effectively capture the user's interest in attention subspaces and generate personalized ranking result. First, we establish three user attention subspaces and extract audio, visual, temporal affective features to represent the human perception of highlight in each subspace. Then, the highlight ranking models are constructed using support vector regression (SVR) for the three subspaces respectively. Finally, the three submodels are linearly combined to generate the final ranking model. Relevance feedback technique is employed to adjust the weights of each submodel to obtain the result which is suitable to the user's preference. Experimental results demonstrate our approach is effective.