TY - GEN
T1 - Sports video mining with mosaic
AU - Mei, Tao
AU - Ma, Yu-Fei
AU - Zhou, He-Qin
AU - Ma, Wei-Ying
AU - Zhang, Hong-Jiang
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2005
Y1 - 2005
N2 - Video is an information-intensive media with much redundancy. Therefore, it is desirable to be able to mine structure or semantics of video data for efficient browsing, summarization and highlight extraction. In this paper, we propose a generic approach to key-event as well as structure mining for sports video analysis. Mosaic is generated for each shot as the representative image of shot content. Based on mosaic, sports video is mined by the method with prior knowledge and without prior knowledge. Without prior knowledge, our system may locate plays by discriminating those segments without essential content, such as breaks. If prior knowledge is available, the key-events in plays are detected using robust features extracted from mosaic. Experimental results have demonstrated the effectiveness and robustness of this sports video mining approach. © 2005 IEEE.
AB - Video is an information-intensive media with much redundancy. Therefore, it is desirable to be able to mine structure or semantics of video data for efficient browsing, summarization and highlight extraction. In this paper, we propose a generic approach to key-event as well as structure mining for sports video analysis. Mosaic is generated for each shot as the representative image of shot content. Based on mosaic, sports video is mined by the method with prior knowledge and without prior knowledge. Without prior knowledge, our system may locate plays by discriminating those segments without essential content, such as breaks. If prior knowledge is available, the key-events in plays are detected using robust features extracted from mosaic. Experimental results have demonstrated the effectiveness and robustness of this sports video mining approach. © 2005 IEEE.
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U2 - 10.1109/MMMC.2005.68
DO - 10.1109/MMMC.2005.68
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 0769521649
SN - 9780769521640
T3 - Proceedings of the 11th International Multimedia Modelling Conference, MMM 2005
SP - 107
EP - 114
BT - Proceedings of the 11th International Multimedia Modelling Conference, MMM 2005
T2 - 11th International Multimedia Modelling Conference, MMM 2005
Y2 - 12 January 2005 through 14 January 2005
ER -