Event threading within news topics
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Parameter free bursty events detection in text streams
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Tracking news stories across different sources
Proceedings of the 13th annual ACM international conference on Multimedia
Hot Topic Extraction Based on Timeline Analysis and Multidimensional Sentence Modeling
IEEE Transactions on Knowledge and Data Engineering
Analyzing feature trajectories for event detection
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Scalable detection of partial near-duplicate videos by visual-temporal consistency
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Beyond search: Event-driven summarization for web videos
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Eventscapes: visualizing events over time with emotive facets
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Cross media hyperlinking for search topic browsing
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Galaxy browser: exploratory search of web videos
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Socially motivated multimedia topic timeline summarization
Proceedings of the 2nd international workshop on Socially-aware multimedia
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Search engines are used to return a long list of hundreds or even thousands of videos in response to a query topic. Efficient navigation of videos becomes difficult and users often need to painstakingly explore the search list for a gist of the search result. This paper addresses the challenge of topical summarization by providing a timeline-based visualization of videos through matching of heterogeneous sources. To overcome the so called sparse-text problem of web videos, auxiliary information from Google context is exploited. Google Trends is used to predict the milestone events of a topic. Meanwhile, the typical scenes of web videos are extracted by visual near-duplicate threading. Visual-text alignment is then conducted to align scenes from videos and articles from Google News. The outcome is a set of scene-news pairs, each representing an event mapped to the milestone timeline of a topic. The timeline-based visualization provides a glimpse of major events about a topic. We conduct both the quantitative and subjective studies to evaluate the practicality of the application.