Important Scene Prediction of Baseball Videos Using Twitter and Video Analysis Based on LSTM

This paper presents a novel important scene prediction method using baseball videos and tweets on Twitter based on a Long Short-Term Memory (LSTM). For considering baseball videos and tweets, the proposed method utilizes textual, visual and audio features. Introducing these multi-modal features into the important scene prediction of baseball videos is the first work. In order to deal with multi-modal time-series features constructed from textual, visual and audio features, the proposed method adopts LSTM which is effective for training such multimodal time-series features by maintaining a long-term memory. The effectiveness of the proposed method is confirmed by experimental results.