Automatically extracting highlights for TV Baseball programs
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic detection of 'Goal' segments in basketball videos
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Techniques and Systems for Image and Video Retrieval
IEEE Transactions on Knowledge and Data Engineering
A Survey on Content-Based Retrieval for Multimedia Databases
IEEE Transactions on Knowledge and Data Engineering
An integrated baseball digest system using maximum entropy method
Proceedings of the tenth ACM international conference on Multimedia
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Semantic annotation of soccer videos: automatic highlights identification
Computer Vision and Image Understanding - Special isssue on video retrieval and summarization
Efficient Multimodal Features for Automatic Soccer Highlight Generation
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
The fusion of audio-visual features and external knowledge for event detection in team sports video
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
Learning query-class dependent weights in automatic video retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
A fusion scheme of visual and auditory modalities for event detection in sports video
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
Video search reranking via information bottleneck principle
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Live sports event detection based on broadcast video and web-casting text
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
A comparison of score, rank and probability-based fusion methods for video shot retrieval
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
Event based indexing of broadcasted sports video by intermodalcollaboration
IEEE Transactions on Multimedia
Personalized abstraction of broadcasted American football video by highlight selection
IEEE Transactions on Multimedia
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Spatio-temporal pyramid matching for sports videos
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Multimedia Tools and Applications
Personalized retrieval of sports video based on multi-modal analysis and user preference acquisition
Multimedia Tools and Applications
Personalized sports video customization for mobile devices
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
A spatio-temporal pyramid matching for video retrieval
Computer Vision and Image Understanding
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There has been a growing demand for effective access to video information from media archives in recent years. Personalized video retrieval is one of the most challenging issues and has spurred a significant interest in many research communities. In this paper, a novel approach is proposed to achieve personalized retrieval of sports video, which includes two research tasks: semantic annotation of sports video and acquisition of user's preference. For semantic annotation, a multi-modal framework is employed to detect sports event and index the sports video content. Web-casting text, as external information, is utilized to detect semantic events in sport videos. The semantic concepts and keywords included in the web-casting text are extracted to annotate and index the sport event segments automatically. For user's preference acquisition, relevance feedback is applied to model user's preference and non-preference, and re-ranking is used to refine the results. First, the user is asked to label some video segments as desirable and undesirable. Then, we use these labels to infer the user's interesting points (e.g. the player, the event type, the team, etc.) by analysis of text keywords; the low-level video features are also adopted as a supplementary to reflect the user's preference. The overall new rank of the results is the combination of the user's high-level and low-level preference. Experiments conducted on real-world soccer game videos show that the proposed method has an encouraging performance.