{"title":"利用颜色矩和哈希的实时商业识别","authors":"Abhishek Shivadas, J. Gauch","doi":"10.1109/CRV.2007.53","DOIUrl":null,"url":null,"abstract":"In this paper, our focus is on real-time commercial recognition. In particular, our goal is to correctly identify all commercials that are stored in our commercial database within the first second of their broadcast. To meet this objective, we make use of 27 color moments to characterize the content of every video frame. This representation is much more compact than most color histogram representations, and it less sensitive to noise and other distortion. We use frame-level hashing with subsequent matching of moment vectors and video frames to perform commercial recognition. Hashing provides constant time access to millions of video frames, so this approach can perform in real-time for databases containing thousands of commercials. In our experiments with a database of 63 commercials, we achieved 96% recall, 100% precision, and 98% utility while recognizing commercials within the first 1/2 second of their broadcast.","PeriodicalId":304254,"journal":{"name":"Fourth Canadian Conference on Computer and Robot Vision (CRV '07)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"32","resultStr":"{\"title\":\"Real-Time Commercial Recognition Using Color Moments and Hashing\",\"authors\":\"Abhishek Shivadas, J. Gauch\",\"doi\":\"10.1109/CRV.2007.53\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, our focus is on real-time commercial recognition. In particular, our goal is to correctly identify all commercials that are stored in our commercial database within the first second of their broadcast. To meet this objective, we make use of 27 color moments to characterize the content of every video frame. This representation is much more compact than most color histogram representations, and it less sensitive to noise and other distortion. We use frame-level hashing with subsequent matching of moment vectors and video frames to perform commercial recognition. Hashing provides constant time access to millions of video frames, so this approach can perform in real-time for databases containing thousands of commercials. In our experiments with a database of 63 commercials, we achieved 96% recall, 100% precision, and 98% utility while recognizing commercials within the first 1/2 second of their broadcast.\",\"PeriodicalId\":304254,\"journal\":{\"name\":\"Fourth Canadian Conference on Computer and Robot Vision (CRV '07)\",\"volume\":\"9 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-05-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"32\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Fourth Canadian Conference on Computer and Robot Vision (CRV '07)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CRV.2007.53\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Fourth Canadian Conference on Computer and Robot Vision (CRV '07)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CRV.2007.53","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Real-Time Commercial Recognition Using Color Moments and Hashing
In this paper, our focus is on real-time commercial recognition. In particular, our goal is to correctly identify all commercials that are stored in our commercial database within the first second of their broadcast. To meet this objective, we make use of 27 color moments to characterize the content of every video frame. This representation is much more compact than most color histogram representations, and it less sensitive to noise and other distortion. We use frame-level hashing with subsequent matching of moment vectors and video frames to perform commercial recognition. Hashing provides constant time access to millions of video frames, so this approach can perform in real-time for databases containing thousands of commercials. In our experiments with a database of 63 commercials, we achieved 96% recall, 100% precision, and 98% utility while recognizing commercials within the first 1/2 second of their broadcast.