{"title":"Efficient key-frame extraction and video analysis","authors":"J. Calic, E. Izquierdo","doi":"10.1109/ITCC.2002.1000355","DOIUrl":null,"url":null,"abstract":"Content-based video indexing and retrieval has its foundations in the analyses of the prime video temporal structures. Consequently, technologies for video segmentation and key-frame extraction have become crucial for the development of advanced digital video systems. Conventional algorithms for video partitioning and key-frame extraction are mainly implemented autonomously. By focusing the analysis on compressed video features, this paper introduces a real-time algorithm for scene change detection and key-frame extraction that generates frame difference metrics by analysing the statistics of the macro-block features extracted from an MPEG compressed stream. The key-frame extraction method is implemented using difference metrics in curve simplification by means of a discrete contour evolution algorithm. This approach resulted in a fast and robust algorithm. Results of computer simulations are reported.","PeriodicalId":115190,"journal":{"name":"Proceedings. International Conference on Information Technology: Coding and Computing","volume":"203 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"100","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. International Conference on Information Technology: Coding and Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITCC.2002.1000355","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 100
Abstract
Content-based video indexing and retrieval has its foundations in the analyses of the prime video temporal structures. Consequently, technologies for video segmentation and key-frame extraction have become crucial for the development of advanced digital video systems. Conventional algorithms for video partitioning and key-frame extraction are mainly implemented autonomously. By focusing the analysis on compressed video features, this paper introduces a real-time algorithm for scene change detection and key-frame extraction that generates frame difference metrics by analysing the statistics of the macro-block features extracted from an MPEG compressed stream. The key-frame extraction method is implemented using difference metrics in curve simplification by means of a discrete contour evolution algorithm. This approach resulted in a fast and robust algorithm. Results of computer simulations are reported.