Content-Based Attention Ranking Using Visual and Contextual Attention Model for Baseball Videos

The attention analysis of multimedia data is challenging since different models have to be constructed according to different attention characteristics. This paper analyzes how people are excited about the watched video content and proposes a content-driven attention ranking strategy which enables client users to iteratively browse the video according to their preference. The proposed attention rank (AR) algorithm, which is extended from the Google PageRank algorithm that sorts the websites based on the importance, can effectively measure the user interest (UI) level for each video frame. The degree of attention is derived by integrating the object-based visual attention model (VAM) with the contextual attention model (CAM), which not only can more reliably take advantage of the human perceptual characteristics, but also can effectively identify which video content may attract users' attention. The information of users' feedback is utilized in re-ranking procedure to further improve the retrieving accuracy. The proposed algorithm is specifically evaluated on broadcasted baseball videos.