%0 Journal Article %1 ZhangXuEtAl09mtaa %A Zhang, Yi-Fan %A Xu, Changsheng %A Zhang, Xiaoyu %A Lu, Hanqing %D 2009 %J Multimedia Tools and Applications %K v1205 springer paper ai multimedia interaction adaptive user interface video text analysis semantic processing information retrieval %N 2 %P 305-330 %R 10.1007/s11042-009-0291-y %T Personalized Retrieval of Sports Video Based on Multi-modal Analysis and User Preference Acquisition %V 44 %X In this paper, we present a novel framework on personalized retrieval of sports video, which includes two research tasks: semantic annotation and user preference acquisition. For semantic annotation, web-casting texts which are corresponding tosports videos are firstly captured from the webpages using data region segmentation and labeling. Incorporating the text, we detect events in the sports video and generate video event clips. These video clips are annotated by the semantics extracted from web-casting texts and indexed in a sports video database. Based on the annotation, these video clips can be retrieved from different semantic attributes according to the user preference. For user preference acquisition, we utilize click-through data as a feedback from the user. Relevance feedback is applied on text annotation and visual features to infer the intention and interested points of the user. A user preference model is learned to re-rank the initial results. Experiments are conducted on broadcast soccer and basketball videos and show an encouraging performance of the proposed method.

@article{ZhangXuEtAl09mtaa, abstract = {In this paper, we present a novel framework on personalized retrieval of sports video, which includes two research tasks: semantic annotation and user preference acquisition. For semantic annotation, web-casting texts which are corresponding tosports videos are firstly captured from the webpages using data region segmentation and labeling. Incorporating the text, we detect events in the sports video and generate video event clips. These video clips are annotated by the semantics extracted from web-casting texts and indexed in a sports video database. Based on the annotation, these video clips can be retrieved from different semantic attributes according to the user preference. For user preference acquisition, we utilize click-through data as a feedback from the user. Relevance feedback is applied on text annotation and visual features to infer the intention and interested points of the user. A user preference model is learned to re-rank the initial results. Experiments are conducted on broadcast soccer and basketball videos and show an encouraging performance of the proposed method.}, added-at = {2012-05-30T10:56:15.000+0200}, author = {Zhang, Yi-Fan and Xu, Changsheng and Zhang, Xiaoyu and Lu, Hanqing}, biburl = {https://www.bibsonomy.org/bibtex/2dff34005f17401cae2b8c857988cf2c2/flint63}, doi = {10.1007/s11042-009-0291-y}, file = {SpringerLink:2009/ZhangXuEtAl09mtaa.pdf:PDF}, groups = {public}, interhash = {7dc05c8714dcb49019f5bfb06dd2c204}, intrahash = {dff34005f17401cae2b8c857988cf2c2}, issn = {1380-7501}, journal = {Multimedia Tools and Applications}, keywords = {v1205 springer paper ai multimedia interaction adaptive user interface video text analysis semantic processing information retrieval}, number = 2, pages = {305-330}, timestamp = {2018-04-16T12:34:12.000+0200}, title = {Personalized Retrieval of Sports Video Based on Multi-modal Analysis and User Preference Acquisition}, username = {flint63}, volume = 44, year = 2009 }