Enhancing the MST-CSS Representation Using Robust Geometric Features, for Efficient Content Based Video Retrieval (CBVR)

C. Chattopadhyay, Sukhendu Das
{"title":"Enhancing the MST-CSS Representation Using Robust Geometric Features, for Efficient Content Based Video Retrieval (CBVR)","authors":"C. Chattopadhyay, Sukhendu Das","doi":"10.1109/ISM.2012.71","DOIUrl":null,"url":null,"abstract":"Multi-Spectro-Temporal Curvature Scale Space (MST-CSS) had been proposed as a video content descriptor in an earlier work, where the peak and saddle points were used for feature points. But these are inadequate to capture the salient features of the MST-CSS surface, producing poor retrieval results. To overcome these, we propose EMST-CSS (Enhanced MST-CSS) as a better feature representation with an improved matching method for CBVR (Content Based Video Retrieval). Comparative study with the existing MST-CSS representation and two state-of-the-art methods for CBVR shows enhanced performance on one synthetic and two real-world datasets.","PeriodicalId":282528,"journal":{"name":"2012 IEEE International Symposium on Multimedia","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE International Symposium on Multimedia","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISM.2012.71","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6

Abstract

Multi-Spectro-Temporal Curvature Scale Space (MST-CSS) had been proposed as a video content descriptor in an earlier work, where the peak and saddle points were used for feature points. But these are inadequate to capture the salient features of the MST-CSS surface, producing poor retrieval results. To overcome these, we propose EMST-CSS (Enhanced MST-CSS) as a better feature representation with an improved matching method for CBVR (Content Based Video Retrieval). Comparative study with the existing MST-CSS representation and two state-of-the-art methods for CBVR shows enhanced performance on one synthetic and two real-world datasets.
利用鲁棒几何特征增强MST-CSS表示,实现基于内容的高效视频检索(CBVR)
多光谱-时间曲率尺度空间(MST-CSS)在较早的研究中被提出作为视频内容描述符,其中峰点和鞍点作为特征点。但这些都不足以捕捉到MST-CSS表面的显著特征,导致检索结果不佳。为了克服这些问题,我们提出了EMST-CSS (Enhanced MST-CSS)作为CBVR(基于内容的视频检索)的更好的特征表示和改进的匹配方法。与现有的MST-CSS表示和两种最先进的CBVR方法进行比较研究表明,在一个合成数据集和两个真实数据集上,CBVR的性能有所提高。
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 求助全文
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信