R. Glasberg, S. Schmiedeke, Martin Mocigemba, T. Sikora
{"title":"New Real-Time Approaches for Video-Genre-Classification Using High-Level Descriptors and a Set of Classifiers","authors":"R. Glasberg, S. Schmiedeke, Martin Mocigemba, T. Sikora","doi":"10.1109/ICSC.2008.92","DOIUrl":null,"url":null,"abstract":"In this paper we describe in detail the recent publications related to video-genre-classification and present our improved new approaches for classifying video sequences in real-time as 'cartoon', 'commercial', 'music', 'news' or 'sport' by analyzing the content with new high-level audio-visual descriptors and classification methods. Such applications have also been discussed in the context of MPEG-7. The results demonstrate identification rates of more than 90% based on a large representative collection of 100 videos gathered from free digital TV and Internet.","PeriodicalId":102805,"journal":{"name":"2008 IEEE International Conference on Semantic Computing","volume":"127 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"16","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 IEEE International Conference on Semantic Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSC.2008.92","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 16
Abstract
In this paper we describe in detail the recent publications related to video-genre-classification and present our improved new approaches for classifying video sequences in real-time as 'cartoon', 'commercial', 'music', 'news' or 'sport' by analyzing the content with new high-level audio-visual descriptors and classification methods. Such applications have also been discussed in the context of MPEG-7. The results demonstrate identification rates of more than 90% based on a large representative collection of 100 videos gathered from free digital TV and Internet.