{"title":"用于内容识别的规范化Adaboost","authors":"Honghai Yu, P. Moulin","doi":"10.1109/ICASSP.2013.6638224","DOIUrl":null,"url":null,"abstract":"This paper proposes a regularized Adaboost learning algorithm to extract binary fingerprints by filtering and quantizing perceptually significant features. The proposed algorithm extends the recent symmetric pairwise boosting (SPB) algorithm by taking feature sequence correlation into account. Information and learning theoretic analysis is given. Significant performance gains over SPB are demonstrated for both audio and video fingerprinting.","PeriodicalId":183968,"journal":{"name":"2013 IEEE International Conference on Acoustics, Speech and Signal Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Regularized Adaboost for content identification\",\"authors\":\"Honghai Yu, P. Moulin\",\"doi\":\"10.1109/ICASSP.2013.6638224\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes a regularized Adaboost learning algorithm to extract binary fingerprints by filtering and quantizing perceptually significant features. The proposed algorithm extends the recent symmetric pairwise boosting (SPB) algorithm by taking feature sequence correlation into account. Information and learning theoretic analysis is given. Significant performance gains over SPB are demonstrated for both audio and video fingerprinting.\",\"PeriodicalId\":183968,\"journal\":{\"name\":\"2013 IEEE International Conference on Acoustics, Speech and Signal Processing\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-10-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 IEEE International Conference on Acoustics, Speech and Signal Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICASSP.2013.6638224\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE International Conference on Acoustics, Speech and Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICASSP.2013.6638224","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper proposes a regularized Adaboost learning algorithm to extract binary fingerprints by filtering and quantizing perceptually significant features. The proposed algorithm extends the recent symmetric pairwise boosting (SPB) algorithm by taking feature sequence correlation into account. Information and learning theoretic analysis is given. Significant performance gains over SPB are demonstrated for both audio and video fingerprinting.
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