Semantic analysis for video contents extraction—spotting by association in news video
MULTIMEDIA '97 Proceedings of the fifth ACM international conference on Multimedia
Topic labeling of broadcast news stories in the informedia digital video library
Proceedings of the third ACM conference on Digital libraries
Semantic Structures for Video Data Indexing
AMCP '98 Proceedings of the First International Conference on Advanced Multimedia Content Processing
A TV News Recommendation System with Automatic Recomposition
AMCP '98 Proceedings of the First International Conference on Advanced Multimedia Content Processing
Story Segmentation and Detection of Commercials in Broadcast News Video
ADL '98 Proceedings of the Advances in Digital Libraries Conference
Video Skimming and Characterization through the Combination of Image and Language Understanding
CAIVD '98 Proceedings of the 1998 International Workshop on Content-Based Access of Image and Video Databases (CAIVD '98)
A TV Program Generation System using Digest of Video Scenes and a Scripting Markup Language
HICSS '01 Proceedings of the 34th Annual Hawaii International Conference on System Sciences ( HICSS-34)-Volume 4 - Volume 4
Automatic Classification of Tv News Articles Based on Telop Character Recognition
ICMCS '99 Proceedings of the 1999 IEEE International Conference on Multimedia Computing and Systems - Volume 02
Speaker Indexing for News Articles, Debates and Drama in Broadcasted Tv Programs
ICMCS '99 Proceedings of the 1999 IEEE International Conference on Multimedia Computing and Systems - Volume 02
Personalized digests of sports programs using intuitive retrieval and semantic analysis
ER'00 Proceedings of the 19th international conference on Conceptual modeling
Personal Digest System for Professional Baseball Programs in Mobile Environment
MDM '03 Proceedings of the 4th International Conference on Mobile Data Management
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Content providers have recently started adding a variety of meta data to various video programs; these data provide primitive descriptors of the video contents. Personal digest viewing that uses the meta data is a new application in the digital broadcasting era. To build personal digests, semantic program structures must be constructed and significant scenes must be identified. Digests are currently made manually at content provider sites. This is time-consuming and increases the cost. This paper proposes a way to solve these problems with a rule-based personal digest-making scheme (PDMS) that can automatically and dynamically make personal digests from the meta data. In PDMS, depending on properties of the video program contents and viewer preferences, high-level semantic program structures can be constructed from the added primitive meta data and significant scenes can be extracted. The paper illustrates a formal PDMS model. It also presents detailed evaluation results of PDMS using the contents of a professional baseball game TV program.