NeuralStory:一个用于视频索引和再利用的交互式多媒体系统

L. Baraldi, C. Grana, R. Cucchiara
{"title":"NeuralStory:一个用于视频索引和再利用的交互式多媒体系统","authors":"L. Baraldi, C. Grana, R. Cucchiara","doi":"10.1145/3095713.3095735","DOIUrl":null,"url":null,"abstract":"In the last years video has been swamping the Internet: websites, social networks, and business multimedia systems are adopting video as the most important form of communication and information. Video are normally accessed as a whole and are not indexed in the visual content. Thus, they are often uploaded as short, manually cut clips with user-provided annotations, keywords and tags for retrieval. In this paper, we propose a prototype multimedia system which addresses these two limitations: it overcomes the need of human intervention in the video setting, thanks to fully deep learning-based solutions, and decomposes the storytelling structure of the video into coherent parts. These parts can be shots, key-frames, scenes and semantically related stories, and are exploited to provide an automatic annotation of the visual content, so that parts of video can be easily retrieved. This also allows a principled re-use of the video itself: users of the platform can indeed produce new storytelling by means of multi-modal presentations, add text and other media, and propose a different visual organization of the content. We present the overall solution, and some experiments on the re-use capability of our platform in edutainment by conducting an extensive user valuation","PeriodicalId":310224,"journal":{"name":"Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing","volume":"33 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"NeuralStory: an Interactive Multimedia System for Video Indexing and Re-use\",\"authors\":\"L. Baraldi, C. Grana, R. Cucchiara\",\"doi\":\"10.1145/3095713.3095735\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the last years video has been swamping the Internet: websites, social networks, and business multimedia systems are adopting video as the most important form of communication and information. Video are normally accessed as a whole and are not indexed in the visual content. Thus, they are often uploaded as short, manually cut clips with user-provided annotations, keywords and tags for retrieval. In this paper, we propose a prototype multimedia system which addresses these two limitations: it overcomes the need of human intervention in the video setting, thanks to fully deep learning-based solutions, and decomposes the storytelling structure of the video into coherent parts. These parts can be shots, key-frames, scenes and semantically related stories, and are exploited to provide an automatic annotation of the visual content, so that parts of video can be easily retrieved. This also allows a principled re-use of the video itself: users of the platform can indeed produce new storytelling by means of multi-modal presentations, add text and other media, and propose a different visual organization of the content. We present the overall solution, and some experiments on the re-use capability of our platform in edutainment by conducting an extensive user valuation\",\"PeriodicalId\":310224,\"journal\":{\"name\":\"Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing\",\"volume\":\"33 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-06-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3095713.3095735\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3095713.3095735","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7

摘要

在过去的几年里,视频已经席卷了互联网:网站、社交网络和商业多媒体系统正在采用视频作为最重要的通信和信息形式。视频通常是作为一个整体访问的,而不是在视觉内容中索引。因此,它们通常被上传为简短的、手动剪辑的片段,并附有用户提供的注释、关键字和标签,以供检索。在本文中,我们提出了一个原型多媒体系统,解决了这两个限制:它克服了对视频设置中人为干预的需要,这要归功于完全基于深度学习的解决方案,并将视频的故事结构分解为连贯的部分。这些部分可以是镜头、关键帧、场景和语义相关的故事,并用于提供视觉内容的自动注释,以便可以轻松检索视频的部分。这也允许对视频本身进行有原则的重用:平台的用户确实可以通过多模态呈现方式产生新的故事,添加文本和其他媒体,并提出不同的内容视觉组织。我们提出了整体解决方案,并通过进行广泛的用户评估,对我们的教育娱乐平台的重用能力进行了一些实验
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
NeuralStory: an Interactive Multimedia System for Video Indexing and Re-use
In the last years video has been swamping the Internet: websites, social networks, and business multimedia systems are adopting video as the most important form of communication and information. Video are normally accessed as a whole and are not indexed in the visual content. Thus, they are often uploaded as short, manually cut clips with user-provided annotations, keywords and tags for retrieval. In this paper, we propose a prototype multimedia system which addresses these two limitations: it overcomes the need of human intervention in the video setting, thanks to fully deep learning-based solutions, and decomposes the storytelling structure of the video into coherent parts. These parts can be shots, key-frames, scenes and semantically related stories, and are exploited to provide an automatic annotation of the visual content, so that parts of video can be easily retrieved. This also allows a principled re-use of the video itself: users of the platform can indeed produce new storytelling by means of multi-modal presentations, add text and other media, and propose a different visual organization of the content. We present the overall solution, and some experiments on the re-use capability of our platform in edutainment by conducting an extensive user valuation
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
相关文献
二甲双胍通过HDAC6和FoxO3a转录调控肌肉生长抑制素诱导肌肉萎缩
IF 8.9 1区 医学Journal of Cachexia, Sarcopenia and MusclePub Date : 2021-11-02 DOI: 10.1002/jcsm.12833
Min Ju Kang, Ji Wook Moon, Jung Ok Lee, Ji Hae Kim, Eun Jeong Jung, Su Jin Kim, Joo Yeon Oh, Sang Woo Wu, Pu Reum Lee, Sun Hwa Park, Hyeon Soo Kim
具有疾病敏感单倍型的非亲属供体脐带血移植后的1型糖尿病
IF 3.2 3区 医学Journal of Diabetes InvestigationPub Date : 2022-11-02 DOI: 10.1111/jdi.13939
Kensuke Matsumoto, Taisuke Matsuyama, Ritsu Sumiyoshi, Matsuo Takuji, Tadashi Yamamoto, Ryosuke Shirasaki, Haruko Tashiro
封面:蛋白质组学分析确定IRSp53和fastin是PRV输出和直接细胞-细胞传播的关键
IF 3.4 4区 生物学ProteomicsPub Date : 2019-12-02 DOI: 10.1002/pmic.201970201
Fei-Long Yu, Huan Miao, Jinjin Xia, Fan Jia, Huadong Wang, Fuqiang Xu, Lin Guo
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Tag Propagation Approaches within Speaking Face Graphs for Multimodal Person Discovery A free Web API for single and multi-document summarization Visualizing weakly-Annotated Multi-label Mayan Inscriptions with Supervised t-SNE Prediction of User Demographics from Music Listening Habits Detecting adversarial example attacks to deep neural networks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1