R. Ji, Yi Yang, N. Sebe, K. Aizawa, Liangliang Cao
With the advance of the Web 2.0 era came an explosive growth of geographical multimedia data shared on social network websites such as Flickr, YouTube, Facebook, and Zooomr. Location-aware media description, modeling, learning, and recommendation in pervasive social media analytics have become a key focus of the recent research in computer vision, multimedia, and signal processing societies. A new breed of multimedia applications that incorporates image/video annotation, visual search, content mining and recommendation, and so on may revolutionize the field. Combined with the popularity of location-aware social multimedia, location context data makes traditionally challenging problems more tractable. This special issue brings together active researchers to share recent progress in this exciting area. This issue highlights the latest developments in large-scale multiple evidence-based learning for geosocial multimedia computing and identifies several key challenges and potential innovations.
{"title":"Large-Scale Geosocial Multimedia [Guest editorial]","authors":"R. Ji, Yi Yang, N. Sebe, K. Aizawa, Liangliang Cao","doi":"10.1109/MMUL.2014.43","DOIUrl":"https://doi.org/10.1109/MMUL.2014.43","url":null,"abstract":"With the advance of the Web 2.0 era came an explosive growth of geographical multimedia data shared on social network websites such as Flickr, YouTube, Facebook, and Zooomr. Location-aware media description, modeling, learning, and recommendation in pervasive social media analytics have become a key focus of the recent research in computer vision, multimedia, and signal processing societies. A new breed of multimedia applications that incorporates image/video annotation, visual search, content mining and recommendation, and so on may revolutionize the field. Combined with the popularity of location-aware social multimedia, location context data makes traditionally challenging problems more tractable. This special issue brings together active researchers to share recent progress in this exciting area. This issue highlights the latest developments in large-scale multiple evidence-based learning for geosocial multimedia computing and identifies several key challenges and potential innovations.","PeriodicalId":290893,"journal":{"name":"IEEE Multim.","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131792862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Incoming Editor in Chief Yong Rui shares his plans for IEEE MultiMedia magazine during his term as well as some of his thoughts, perceived opportunities, and potential action items for 2014.
{"title":"IEEE MultiMedia Forges Ahead","authors":"Y. Rui","doi":"10.1109/MMUL.2014.28","DOIUrl":"https://doi.org/10.1109/MMUL.2014.28","url":null,"abstract":"Incoming Editor in Chief Yong Rui shares his plans for IEEE MultiMedia magazine during his term as well as some of his thoughts, perceived opportunities, and potential action items for 2014.","PeriodicalId":290893,"journal":{"name":"IEEE Multim.","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123554064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Thomas S. Huang, Vuong Le, T. Paine, Pooya Khorrami, U. Tariq
In the early days of multimedia research, the first image dataset collected consisted of only four still grayscale images captured by a drum scanner. At the time, digital imaging was only available in laboratories, and digital videos barely existed. Half a century later, the amount of visual data has exploded at an unprecedented rate. Images and videos are now created, stored, and used by the majority of the population. In this historical overview, the authors follow the great journey that visual media research has embarked upon by looking at the fundamental scientific and engineering inventions. Through this lens, they show that all three aspects of media capturing, delivery, and understanding are developed surrounding the interaction with humans, making visual data processing a particular human-centric field of computing.
{"title":"Visual Media: History and Perspectives","authors":"Thomas S. Huang, Vuong Le, T. Paine, Pooya Khorrami, U. Tariq","doi":"10.1109/MMUL.2014.35","DOIUrl":"https://doi.org/10.1109/MMUL.2014.35","url":null,"abstract":"In the early days of multimedia research, the first image dataset collected consisted of only four still grayscale images captured by a drum scanner. At the time, digital imaging was only available in laboratories, and digital videos barely existed. Half a century later, the amount of visual data has exploded at an unprecedented rate. Images and videos are now created, stored, and used by the majority of the population. In this historical overview, the authors follow the great journey that visual media research has embarked upon by looking at the fundamental scientific and engineering inventions. Through this lens, they show that all three aspects of media capturing, delivery, and understanding are developed surrounding the interaction with humans, making visual data processing a particular human-centric field of computing.","PeriodicalId":290893,"journal":{"name":"IEEE Multim.","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117021632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This special issue focuses on the management and exploitation of multimedia data in mobile computing environments. The main goal was to collect articles reporting the latest advances on the technologies, algorithms, models, standards, and applications for managing and exploiting multimedia data in mobile computing. Specifically, this issue includes four representative articles that encompass topics such as image databases for mobile devices, mobile photo recommendation, logbook generation from context-tagged images, keyword-based multimedia retrieval, and querying multimedia data in vehicular networks.
{"title":"Multimedia Data Management in Mobile Computing","authors":"S. Ilarri, F. Sèdes, F. D. Natale, A. Hanjalic","doi":"10.1109/MMUL.2014.12","DOIUrl":"https://doi.org/10.1109/MMUL.2014.12","url":null,"abstract":"This special issue focuses on the management and exploitation of multimedia data in mobile computing environments. The main goal was to collect articles reporting the latest advances on the technologies, algorithms, models, standards, and applications for managing and exploiting multimedia data in mobile computing. Specifically, this issue includes four representative articles that encompass topics such as image databases for mobile devices, mobile photo recommendation, logbook generation from context-tagged images, keyword-based multimedia retrieval, and querying multimedia data in vehicular networks.","PeriodicalId":290893,"journal":{"name":"IEEE Multim.","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126397301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Video search needs effective and efficient techniques for video summarization to enable rapid triage and finding relevant video contents.
视频搜索需要有效的视频摘要技术，以实现快速分类和查找相关视频内容。
{"title":"Herding Cats","authors":"John R. Smith","doi":"10.1109/MMUL.2013.56","DOIUrl":"https://doi.org/10.1109/MMUL.2013.56","url":null,"abstract":"Video search needs effective and efficient techniques for video summarization to enable rapid triage and finding relevant video contents.","PeriodicalId":290893,"journal":{"name":"IEEE Multim.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129620882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Machine learning has become an indispensible tool for the multimedia community. Given large amounts of data, computers using machine learning are able to create rich representations and accomplish impressive discrimination tasks. Yet, the way machines learn is still differs significantly from how humans learn. EIC John R. Smith explains that the way forward is for the multimedia field to create appropriate lesson plans or more generally develop curriculum-based approaches to multimedia machine learning.
机器学习已经成为多媒体社区不可或缺的工具。给定大量数据，使用机器学习的计算机能够创建丰富的表示并完成令人印象深刻的识别任务。然而，机器学习的方式仍然与人类的学习方式有很大不同。EIC John R. Smith解释说，多媒体领域的前进之路是创建合适的课程计划，或者更广泛地开发基于课程的多媒体机器学习方法。
{"title":"Lessons in Learning","authors":"John R. Smith","doi":"10.1109/MMUL.2013.39","DOIUrl":"https://doi.org/10.1109/MMUL.2013.39","url":null,"abstract":"Machine learning has become an indispensible tool for the multimedia community. Given large amounts of data, computers using machine learning are able to create rich representations and accomplish impressive discrimination tasks. Yet, the way machines learn is still differs significantly from how humans learn. EIC John R. Smith explains that the way forward is for the multimedia field to create appropriate lesson plans or more generally develop curriculum-based approaches to multimedia machine learning.","PeriodicalId":290893,"journal":{"name":"IEEE Multim.","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128327889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
C. Ngo, Changsheng Xu, Wessel Kraaij, Abdulmotaleb El Saddik
This paper presents some of the most recent advances in the research on Web-scale near-duplicate search and explores the potential for bringing this research a substantial step further. It contains high-quality contributions addressing various aspects of the Web-scale near-duplicate search problem in a number of relevant domains. The topics range from feature representation, matching, and indexing from different novel aspects to the adaptation of current technologies for mobile media search and photo archaeology mining.
{"title":"Web-Scale Near-Duplicate Search: Techniques and Applications","authors":"C. Ngo, Changsheng Xu, Wessel Kraaij, Abdulmotaleb El Saddik","doi":"10.1109/MMUL.2013.45","DOIUrl":"https://doi.org/10.1109/MMUL.2013.45","url":null,"abstract":"This paper presents some of the most recent advances in the research on Web-scale near-duplicate search and explores the potential for bringing this research a substantial step further. It contains high-quality contributions addressing various aspects of the Web-scale near-duplicate search problem in a number of relevant domains. The topics range from feature representation, matching, and indexing from different novel aspects to the adaptation of current technologies for mobile media search and photo archaeology mining.","PeriodicalId":290893,"journal":{"name":"IEEE Multim.","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133406044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article summarizes the outcome of the 2012 Shonan Meeting “Future of Multimedia Analysis and Mining.”The meeting was really interesting, and the participants had a fun time with an Kamakura excursion and fine dinners, in addition to in-depth discussions on ready-to-go hot research topics (see Figure 4). We have enjoyed sharing even part of our experiences with readers here.
{"title":"The Future of Multimedia Analysis and Mining: Visions from the Shonan Meeting","authors":"N. Boujemaa, Alexander Hauptmann, S. Satoh","doi":"10.1109/MMUL.2013.30","DOIUrl":"https://doi.org/10.1109/MMUL.2013.30","url":null,"abstract":"This article summarizes the outcome of the 2012 Shonan Meeting “Future of Multimedia Analysis and Mining.”The meeting was really interesting, and the participants had a fun time with an Kamakura excursion and fine dinners, in addition to in-depth discussions on ready-to-go hot research topics (see Figure 4). We have enjoyed sharing even part of our experiences with readers here.","PeriodicalId":290893,"journal":{"name":"IEEE Multim.","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130957427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Growing multicamera, multiperspective image capture provides the opportunity to reconstruct and recount real-world events at a fine resolution.
越来越多的多摄像机，多视角的图像捕捉提供了机会，以良好的分辨率重建和叙述现实世界的事件。
{"title":"Reconstructing Events","authors":"John R. Smith","doi":"10.1109/MMUL.2013.27","DOIUrl":"https://doi.org/10.1109/MMUL.2013.27","url":null,"abstract":"Growing multicamera, multiperspective image capture provides the opportunity to reconstruct and recount real-world events at a fine resolution.","PeriodicalId":290893,"journal":{"name":"IEEE Multim.","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133325665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Multimedia research has taken on many technical problems over the last decade. Problems such as video on demand and face recognition receive less focus today, while others like content-based retrieval and social media are gaining focus. Different factors can help explain the shifting focus in multimedia research.
{"title":"Problems Gone By?","authors":"John R. Smith","doi":"10.1109/MMUL.2012.49","DOIUrl":"https://doi.org/10.1109/MMUL.2012.49","url":null,"abstract":"Multimedia research has taken on many technical problems over the last decade. Problems such as video on demand and face recognition receive less focus today, while others like content-based retrieval and social media are gaining focus. Different factors can help explain the shifting focus in multimedia research.","PeriodicalId":290893,"journal":{"name":"IEEE Multim.","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116449158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：