Abstract
To enable us to select the only scenes that we want to watch in a baseball video and personalize its highlights subvideo, we require an Automatic Baseball Video Tagging system that divides a baseball video into multiple sub-videos per at-bat scene automatically and also appends tag information relevant to at-bat scenes. The previous paper proposed several Tagging algorithms using ball-by-ball textual report and voice recognition. To improve our system, this paper introduces a more refined model for baseball games, and performs comparative experiments on models with regard to their recall, precision, and F-measure.
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