Multimodal Retrieval of Similar Soccer Videos Based on Optimal Combination of Multiple Distance Measures
Tomoki Haruyama, Sho Takahashi, Takahiro Ogawa, Miki Haseyama · 2019
This paper presents a new multimodal method for retrieval of similar soccer videos based on optimal combination of multiple distance measures. Our method first extracts three types of Convolutional Neural Network-based features focusing the players' actions, the audience's cheers and prompt reports. Then, by applying the optimal distance measure to each feature, we calculate the similarities between a query video and videos in a database. Finally, we realize accurate retrieval of similar soccer videos by integrating these similarities. Experiments on actual soccer videos demonstrate encouraging results.