Topic Tracking Across Broadcast News Videos with Visual Duplicates and Semantic Concepts
Videos from distributed sources (e.g., broadcasts, podcasts, blogs, etc.) have grown exponentially. Topic threading is very useful for organizing such large-volume information sources. Current solutions primarily rely on text features only but encounter difficulty when text is noisy or unavailable. In this paper, we propose new representations and similarity measures for news videos based on low-level features, visual near-duplicates, and high-level semantic concepts automatically detected from videos. We develop a multi-modal fusion framework for estimating relevance of a new story to a known topic. Our extensive experiments using TRECVID 2005 data set (171 hours, 6 channels, 3 languages) confirm that near-duplicates consistently and significantly boost the tracking performance by up to 25%. In addition, we present information-theoretic analysis to assess the complexity of each semantic topic and determine the best subset of concepts for tracking each topic.