Automatic text recognition for video indexing
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
Automatically extracting highlights for TV Baseball programs
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
TV anytime as an application scenario for MPEG-7
MULTIMEDIA '00 Proceedings of the 2000 ACM workshops on Multimedia
Linking live and replay scenes in broadcasted sports video
MULTIMEDIA '00 Proceedings of the 2000 ACM workshops on Multimedia
Rule-based video classification system for basketball video indexing
MULTIMEDIA '00 Proceedings of the 2000 ACM workshops on Multimedia
Automatic detection of 'Goal' segments in basketball videos
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Automatic Parsing of TV Soccer Programs
ICMCS '95 Proceedings of the International Conference on Multimedia Computing and Systems
Detection of slow-motion replay segments in sports video for highlights generation
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 03
IEEE Transactions on Circuits and Systems for Video Technology
Constructing a bowling information system with video content analysis
MMDB '03 Proceedings of the 1st ACM international workshop on Multimedia databases
Sports video summarization using highlights and play-breaks
MIR '03 Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval
Constructing a Bowling Information System with Video Content Analysis
Multimedia Tools and Applications
An architecture for TV content distributed search and retrieval using the MPEG query format (MPQF)
Proceedings of the 2008 Ambi-Sys workshop on Ambient media delivery and interactive television
Semantic-Driven Multimedia Retrieval with the MPEG Query Format
SAMT '08 Proceedings of the 3rd International Conference on Semantic and Digital Media Technologies: Semantic Multimedia
Semantic-driven multimedia retrieval with the MPEG Query Format
Multimedia Tools and Applications
Content-based organisation, analysis and retrieval of soccer video
International Journal of Computer Applications in Technology
Knowledge-discounted event detection in sports video
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special issue on model-based diagnostics
Structural and event based multimodal video data modeling
ADVIS'06 Proceedings of the 4th international conference on Advances in Information Systems
Flexible querying using structural and event based multimodal video data model
FQAS'06 Proceedings of the 7th international conference on Flexible Query Answering Systems
Frontiers of Computer Science: Selected Publications from Chinese Universities
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Current advances in multimedia technology enable ease of capturing and encoding digital video. As a result, video data is rapidly growing and becoming very important in our life. It is because video can transfer a large amount of knowledge by providing combination of text, graphics, or even images. Despite the vast growth of video, the effectiveness of its usage is very limited due to the lack of a complete technology for the organization and retrieval of video data. To date, there is no “perfect” solution for a complete video data-management technology, which can fully capture the content of video and index the video parts according to the contents, so that users can intuitively retrieve specific video segments. We have found that successful content-based video data-management systems depend on three most important components: key-segments extraction, content descriptions and video retrieval. While it is almost impossible for current computer technology to perceive the content of the video to identify correctly its key-segments, the system can understand more accurately the content of a specific video type by identifying the typical events that happens just before or after the key-segments (specific-domain-approach). Thus, we have proposed a concept of customisable video segmentation module, which integrates the suitable segmentation techniques for the current type of video. The identified key-segments are then described using standard video content descriptions to enable content-based retrievals. For retrieval, we have implemented XQuery, which currently is the most recent XML query language and the most powerful compared to older languages, such as XQL and XML-QL.