{"title":"Enhanced Semi-Supervised Learning for Automatic Video Annotation","authors":"Meng Wang, Xiansheng Hua, Lirong Dai, Yan Song","doi":"10.1109/ICME.2006.262823","DOIUrl":null,"url":null,"abstract":"For automatic semantic annotation of large-scale video database, the insufficiency of labeled training samples is a major obstacle. General semi-supervised learning algorithms can help solve the problem but the improvement is limited. In this paper, two semi-supervised learning algorithms, self-training and co-training, are enhanced by exploring the temporal consistency of semantic concepts in video sequences. In the enhanced algorithms, instead of individual shots, time-constraint shot clusters are taken as the basic sample units, in which most mis-classifications can be corrected before they are applied for re-training, thus more accurate statistical models can be obtained. Experiments show that enhanced self-training/co-training significantly improves the performance of video annotation","PeriodicalId":339258,"journal":{"name":"2006 IEEE International Conference on Multimedia and Expo","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 IEEE International Conference on Multimedia and Expo","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICME.2006.262823","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 11
Abstract
For automatic semantic annotation of large-scale video database, the insufficiency of labeled training samples is a major obstacle. General semi-supervised learning algorithms can help solve the problem but the improvement is limited. In this paper, two semi-supervised learning algorithms, self-training and co-training, are enhanced by exploring the temporal consistency of semantic concepts in video sequences. In the enhanced algorithms, instead of individual shots, time-constraint shot clusters are taken as the basic sample units, in which most mis-classifications can be corrected before they are applied for re-training, thus more accurate statistical models can be obtained. Experiments show that enhanced self-training/co-training significantly improves the performance of video annotation