{"title":"家庭视频的自动分割","authors":"Y. Zhai, M. Shah","doi":"10.1109/ICME.2005.1521347","DOIUrl":null,"url":null,"abstract":"Temporal video segmentation is one of the fundamental and essential tasks in video processing, understanding and management. In this paper, we present an automatic method for segmenting the home videos into temporal logical units. We have developed a statistical framework using Markov chain Monte Carlo (MCMC) technique. The temporal scene boundaries are detected by maximizing the posterior probability of the model parameters. The model parameters contain the number of the scenes and the boundary locations of the scenes. The proposed method has been demonstrated on several home videos, and high accuracy has been obtained","PeriodicalId":244360,"journal":{"name":"2005 IEEE International Conference on Multimedia and Expo","volume":"81 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Automatic Segmentation of Home Videos\",\"authors\":\"Y. Zhai, M. Shah\",\"doi\":\"10.1109/ICME.2005.1521347\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Temporal video segmentation is one of the fundamental and essential tasks in video processing, understanding and management. In this paper, we present an automatic method for segmenting the home videos into temporal logical units. We have developed a statistical framework using Markov chain Monte Carlo (MCMC) technique. The temporal scene boundaries are detected by maximizing the posterior probability of the model parameters. The model parameters contain the number of the scenes and the boundary locations of the scenes. The proposed method has been demonstrated on several home videos, and high accuracy has been obtained\",\"PeriodicalId\":244360,\"journal\":{\"name\":\"2005 IEEE International Conference on Multimedia and Expo\",\"volume\":\"81 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2005-07-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2005 IEEE International Conference on Multimedia and Expo\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICME.2005.1521347\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2005 IEEE International Conference on Multimedia and Expo","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICME.2005.1521347","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Temporal video segmentation is one of the fundamental and essential tasks in video processing, understanding and management. In this paper, we present an automatic method for segmenting the home videos into temporal logical units. We have developed a statistical framework using Markov chain Monte Carlo (MCMC) technique. The temporal scene boundaries are detected by maximizing the posterior probability of the model parameters. The model parameters contain the number of the scenes and the boundary locations of the scenes. The proposed method has been demonstrated on several home videos, and high accuracy has been obtained
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