#93: Semantic concept mining based on hierarchical event detection for soccer video indexing


Abstract—In this paper, we present a novel automated indexing and semantic labeling for broadcast soccer video se- quences. The proposed method automatically extracts silent events from the video and classifies each event sequence into a concept by sequential association mining. The paper makes three new contributions in multimodal sports video indexing and summarization. First, we propose a novel hierarchical framework for soccer (football) video event sequence detection and classification. Unlike most existing video classification approaches, which focus on shot detection followed by shot-clustering for classification, the proposed scheme perform a top-down video scene classification which avoids shot clustering. This improves the classification accuracy and also maintains the temporal order of shots. Second, we compute the association for the events of each excitement clip using a priori mining algorithm. We pro- pose a novel sequential association distance to classify the association of the excitement clip into semantic concepts. For soccer video, we have considered goal scored by team-A, goal scored by team-B, goal saved by team-A, goal saved by team-B as semantic concepts. Third, the extracted excitement clips with semantic concept label helps us to summarize many hours of video to collection of soccer highlights such as goals, saves, corner kicks, etc. We show promising results, with correctly indexed soccer scenes, enabling structural and temporal analysis, such as video retrieval, highlight extraction, and video skimming.