{"title":"新闻视频故事分割","authors":"Yong Fang, Xiaofei Zhai, Jingwang Fan","doi":"10.1109/MMMC.2006.1651357","DOIUrl":null,"url":null,"abstract":"This paper presents a two-level framework for news video segmentation. Our framework is established-based upon a similar framework as in. We extended the original framework by adding rule-based pre-segmentation module, similarity measurement module and new features. We perform decision tree at the shot level and HMM (hidden Markov models) analysis at the story level, respectively. Experiment result with a training set of 24 hours (967 story units) news video from CCTV-9 (China Central TV-International) and a testing set of 24 hours (779 story units) news video from several TV-channels show that our semi-automatic system can achieve 81.5% of F1 value in the case of CCTV-9","PeriodicalId":107275,"journal":{"name":"2006 12th International Multi-Media Modelling Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"News video story segmentation\",\"authors\":\"Yong Fang, Xiaofei Zhai, Jingwang Fan\",\"doi\":\"10.1109/MMMC.2006.1651357\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a two-level framework for news video segmentation. Our framework is established-based upon a similar framework as in. We extended the original framework by adding rule-based pre-segmentation module, similarity measurement module and new features. We perform decision tree at the shot level and HMM (hidden Markov models) analysis at the story level, respectively. Experiment result with a training set of 24 hours (967 story units) news video from CCTV-9 (China Central TV-International) and a testing set of 24 hours (779 story units) news video from several TV-channels show that our semi-automatic system can achieve 81.5% of F1 value in the case of CCTV-9\",\"PeriodicalId\":107275,\"journal\":{\"name\":\"2006 12th International Multi-Media Modelling Conference\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-07-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2006 12th International Multi-Media Modelling Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MMMC.2006.1651357\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 12th International Multi-Media Modelling Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MMMC.2006.1651357","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper presents a two-level framework for news video segmentation. Our framework is established-based upon a similar framework as in. We extended the original framework by adding rule-based pre-segmentation module, similarity measurement module and new features. We perform decision tree at the shot level and HMM (hidden Markov models) analysis at the story level, respectively. Experiment result with a training set of 24 hours (967 story units) news video from CCTV-9 (China Central TV-International) and a testing set of 24 hours (779 story units) news video from several TV-channels show that our semi-automatic system can achieve 81.5% of F1 value in the case of CCTV-9
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