{"title":"基于三种视觉描述符和决策树的实时商业广告识别","authors":"R. Glasberg, Cengiz Tas, T. Sikora","doi":"10.1109/ICME.2006.262822","DOIUrl":null,"url":null,"abstract":"We present a new approach for classifying mpeg-2 video sequences as `commercial' or `non-commercial' by analyzing specific color, texture and motion features of consecutive frames in real-time. This is part of the well-known video-genre-classification problem, where popular TV-broadcast genres like cartoon, commercial, music, news and sports are studied. Such applications have also been discussed in the context of MPEG-7. In our method the extracted features from three visual descriptors are logically combined using a decision tree to produce a reliable recognition. The results demonstrate a high identification rate based on a large collection of 200 representative video sequences (40 `commercials' and 4*40 `non-commercials') gathered from free digital TV-broadcasting in Germany","PeriodicalId":339258,"journal":{"name":"2006 IEEE International Conference on Multimedia and Expo","volume":"3 1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"Recognizing Commercials in Real-Time using Three Visual Descriptors and a Decision-Tree\",\"authors\":\"R. Glasberg, Cengiz Tas, T. Sikora\",\"doi\":\"10.1109/ICME.2006.262822\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present a new approach for classifying mpeg-2 video sequences as `commercial' or `non-commercial' by analyzing specific color, texture and motion features of consecutive frames in real-time. This is part of the well-known video-genre-classification problem, where popular TV-broadcast genres like cartoon, commercial, music, news and sports are studied. Such applications have also been discussed in the context of MPEG-7. In our method the extracted features from three visual descriptors are logically combined using a decision tree to produce a reliable recognition. The results demonstrate a high identification rate based on a large collection of 200 representative video sequences (40 `commercials' and 4*40 `non-commercials') gathered from free digital TV-broadcasting in Germany\",\"PeriodicalId\":339258,\"journal\":{\"name\":\"2006 IEEE International Conference on Multimedia and Expo\",\"volume\":\"3 1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-07-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2006 IEEE International Conference on Multimedia and Expo\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICME.2006.262822\",\"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 IEEE International Conference on Multimedia and Expo","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICME.2006.262822","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Recognizing Commercials in Real-Time using Three Visual Descriptors and a Decision-Tree
We present a new approach for classifying mpeg-2 video sequences as `commercial' or `non-commercial' by analyzing specific color, texture and motion features of consecutive frames in real-time. This is part of the well-known video-genre-classification problem, where popular TV-broadcast genres like cartoon, commercial, music, news and sports are studied. Such applications have also been discussed in the context of MPEG-7. In our method the extracted features from three visual descriptors are logically combined using a decision tree to produce a reliable recognition. The results demonstrate a high identification rate based on a large collection of 200 representative video sequences (40 `commercials' and 4*40 `non-commercials') gathered from free digital TV-broadcasting in Germany
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