Video Attention Ranking using Visual and Contextual Attention Model for Content-based Sports Videos Mining

In this paper, we propose new video attention modeling and content-driven mining strategies which enable client users to browse the video according to their preference. By integrating the object-based visual attention model (V'AM) with the contextual attention model (CAM), the proposed scheme not only can more reliably take advantage of the human perceptual characteristics but also effectively discriminate which video contents may attract users' attention. In addition, extended from the Google PageRank algorithm which sorts the websites based on the importance, we introduce the so-call content-based attention rank (AR) to effectively measure the user interest (UI) level of each video frame. The information of users' feedback is treated as the enhanced query data to further improve the retrieving accuracy. The proposed algorithm is evaluated on commercial baseball game sequences and produces promising results.