Video Primal Sketch: A generic middle-level representation of video

Zhi Han, Zongben Xu, Song-Chun Zhu
{"title":"Video Primal Sketch: A generic middle-level representation of video","authors":"Zhi Han, Zongben Xu, Song-Chun Zhu","doi":"10.1109/ICCV.2011.6126380","DOIUrl":null,"url":null,"abstract":"This paper presents a middle-level video representation named Video Primal Sketch (VPS), which integrates two regimes of models: i) sparse coding model using static or moving primitives to explicitly represent moving corners, lines, feature points, etc., ii) FRAME/MRF model with spatio-temporal filters to implicitly represent textured motion, such as water and fire, by matching feature statistics, i.e. histograms. This paper makes three contributions: i) learning a dictionary of video primitives as parametric generative model; ii) studying the Spatio-Temporal FRAME (ST-FRAME) model for modeling and synthesizing textured motion; and iii) developing a parsimonious hybrid model for generic video representation. VPS selects the proper representation automatically and is compatible with high-level action representations. In the experiments, we synthesize a series of dynamic textures, reconstruct real videos and show varying VPS over the change of densities causing by the scale transition in videos.","PeriodicalId":6391,"journal":{"name":"2011 International Conference on Computer Vision","volume":"257 1","pages":"1283-1290"},"PeriodicalIF":0.0000,"publicationDate":"2011-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 International Conference on Computer Vision","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCV.2011.6126380","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 12

Abstract

This paper presents a middle-level video representation named Video Primal Sketch (VPS), which integrates two regimes of models: i) sparse coding model using static or moving primitives to explicitly represent moving corners, lines, feature points, etc., ii) FRAME/MRF model with spatio-temporal filters to implicitly represent textured motion, such as water and fire, by matching feature statistics, i.e. histograms. This paper makes three contributions: i) learning a dictionary of video primitives as parametric generative model; ii) studying the Spatio-Temporal FRAME (ST-FRAME) model for modeling and synthesizing textured motion; and iii) developing a parsimonious hybrid model for generic video representation. VPS selects the proper representation automatically and is compatible with high-level action representations. In the experiments, we synthesize a series of dynamic textures, reconstruct real videos and show varying VPS over the change of densities causing by the scale transition in videos.
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
视频原始草图:视频的一般中层表示
本文提出了一种称为视频原始草图(video Primal Sketch, VPS)的中级视频表示方法,该方法集成了两种模型:1)使用静态或移动基元的稀疏编码模型,显式地表示移动的角、线、特征点等;2)使用时空滤波器的FRAME/MRF模型,通过匹配特征统计量,即直方图,隐式地表示纹理运动,如水和火。本文做出了三个贡献:1)学习一个视频原语字典作为参数生成模型;ii)研究用于纹理运动建模和合成的时空框架(ST-FRAME)模型;iii)开发用于通用视频表示的简约混合模型。VPS自动选择适当的表示,并与高级动作表示兼容。在实验中,我们合成了一系列动态纹理,重建了真实视频,并展示了视频中尺度转换引起的密度变化对VPS的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Robust and efficient parametric face alignment Video parsing for abnormality detection From learning models of natural image patches to whole image restoration Discriminative figure-centric models for joint action localization and recognition A general preconditioning scheme for difference measures in deformable registration
×
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