R. Kannan, G. Ghinea, Sridhar Swaminathan, Suresh Kannaiyan
{"title":"Improving video summarization based on user preferences","authors":"R. Kannan, G. Ghinea, Sridhar Swaminathan, Suresh Kannaiyan","doi":"10.1109/NCVPRIPG.2013.6776187","DOIUrl":null,"url":null,"abstract":"Although in the past, several automatic video summarization systems had been proposed to generate video summary, a generic summary based only on low-level features will not satisfy every user. As users' needs or preferences for the summary vastly differ for the same video, a unique personalized and customized video summarization system becomes an urgent need nowadays. To address this urgent need, this paper proposes a novel system for generating unique semantically meaningful video summaries for the same video, that are tailored to the preferences or interests of the users. The proposed system stitches video summary based on summary time span and top-ranked shots that are semantically relevant to the user's preferences. The experimental results on the performance of the proposed video summarization system are encouraging.","PeriodicalId":436402,"journal":{"name":"2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NCVPRIPG.2013.6776187","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
Although in the past, several automatic video summarization systems had been proposed to generate video summary, a generic summary based only on low-level features will not satisfy every user. As users' needs or preferences for the summary vastly differ for the same video, a unique personalized and customized video summarization system becomes an urgent need nowadays. To address this urgent need, this paper proposes a novel system for generating unique semantically meaningful video summaries for the same video, that are tailored to the preferences or interests of the users. The proposed system stitches video summary based on summary time span and top-ranked shots that are semantically relevant to the user's preferences. The experimental results on the performance of the proposed video summarization system are encouraging.