期刊文献+

基于结构保留投影的镜头边界检测

Shot boundary detection based on structure preserving projection
下载PDF
导出
摘要 提出了一种基于结构保留投影的镜头边界检测方法。为了提取更具判别力的视频特征,提出了结构保留投影方法,利用流形结构信息而非空间结构信息构建连接矩阵,利用QR分解方法解决了结构保留投影的奇异值问题;采用支持向量机检测镜头边界。实验结果表明该方法具有很好的检测效果。 Shot boundary detection method based on Structure Perserving Projection(SPP) is proposed. In order to extract more effective video features, the SPP is presented. The adject matix of SPP is constructed based on mainflod structure not on space structure, and the singular value problem of SPP is resloved by QR decomposition method. Shot boundary is recognized with Support Vector Machine(SVM). The experimental results show that the proposed method is powerful and effective for shot boundarv detection
出处 《计算机工程与应用》 CSCD 2012年第32期187-190,共4页 Computer Engineering and Applications
基金 湖南省科技厅科技计划资助项目(No.2011FJ3120 No.2012FJ3021) 湖南省教育厅资助科研项目(No.11C0221)
关键词 镜头边界检测 结构保留投影 QR分解 支持向量机 shot boundary detction structure preserving projection QR decomposition support vector machine
  • 相关文献

参考文献12

  • 1Shekar B H,Sharmila K M,Holla R.Shot boundary detection algorithm based on color texture moments[J].Communi-cations in Computer and Information Science,2011,142(3):591-594.
  • 2冯扬,罗森林,王丽萍,潘丽敏.一种新的自适应镜头边界检测算法[J].北京理工大学学报,2010,30(1):100-104. 被引量:6
  • 3Jiang X H,Sun T F,Li J H.A novel shot edge detection algorithm based on chi-square histogram and macro-block statistics[C]//Proc of2008International Symposium on Information Science and Engineering.Washington,USA:IEEE Computer Society,2008:604-607.
  • 4Sakarya U,Telatar Z.Graph-based multilevel temporal video segmentation[J].Multimedia Systems,2008,14(5):277-290.
  • 5Zheng W H,Wing W Y,Patrick P K,et al.Video shot boundary detection using RBFNN minimizing the L-GEM[C]//Proc of2010International Conference on Machine Learning and Cybernetics.Washington,USA:IEEE Computer Society,2010:2156-2160.
  • 6陈萍,李秀强,肖国强,江健民.基于视觉注意特征和SVM的镜头边界检测算法[J].计算机工程与应用,2010,46(7):184-186. 被引量:3
  • 7He X F,Niyogi P.Locality preserving projections[C]//Proc of Neural Information Processing Systems,Vancouver,Canada,2003:153-160.
  • 8王玲,薄列峰,焦李成.密度敏感的半监督谱聚类[J].软件学报,2007,18(10):2412-2422. 被引量:94
  • 9Ye J P,Li Q.A two-stage linear discriminant analysis via QR-decomposition[J].IEEE Trans on Pattern Analysis and machine Intelligence,2005,27(6):929-941.
  • 10Hsu C W,Chang C C,Lin C J.A practical guide to support vector classification[R].Department of Computer Science,National Taiwan University,2003.

二级参考文献22

  • 1佟子健,袁进辉,郑武杰,林福宗,张钹.一种基于有限自动机的渐变镜头检测算法[J].计算机科学,2006,33(1):252-254. 被引量:3
  • 2耿玉亮,须德,冯松鹤.一种快速有效的视频镜头边界检测方法[J].电子学报,2006,34(12):2272-2277. 被引量:11
  • 3Lienhart R, Zaccarin A. A system for reliable dissolve detection in videos[C]//Proceedings of IEEE ICIP2001. Salonika, Greece, IEEE Computer Society, 2001 : 406 -409.
  • 4Zhang H J, Kankanhalli A, Smoliar S W. Automatic partitioning of full-motion video [J]. Multimedia Systems, 1993,1(1):10- 28.
  • 5Youm S, Kim W. Dynamic threshold method for scene change detection[C] // Proceedings of IEEE ICME' 03. Baltimore, USA: IEEE Computer Society, 2003: 337- 340.
  • 6Sze K W, Lam K M, Qiu G. Scene cut detection using the colored pattern appearance model[C] // Proceedings of the IEEE ICIP2003. Barcelona, Spain: IEEE Computer Society, 2003 : 1017 - 1020.
  • 7Yu SX, Shi J. Segmentation given partial grouping constraints. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2004, 26(2): 173-183.
  • 8Hertz T, Shental N, Bar-Hillel A, Weinshall D. Enhancing image and video retrieval: Learning via equivalence constraint. In: Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition. Madison: IEEE Computer Society, 2003.668-674.
  • 9Wagstaff K, Cardie C, Rogers S, Schroedl S. Constrained K-means clustering with background knowledge. In: Brodley CE, Danyluk AP, eds. Proc. of the 18th Int'l Conf. on Machine Learning. Williamstown: Morgan Kaufmann Publishers, 2001. 577-584.
  • 10Klein D, Kamvar SD, Manning CD. From instance-level constraints to space-level constraints: Making the most of prior knowledge in data clustering. In: Sammut C, Hoffmann AG, eds. Proc. of the 19th Int'l Conf. on Machine Learning. Sydney: Morgan Kaufmann Publishers, 2002. 307-314.

共引文献99

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部