期刊文献+

新闻视频相似关键帧识别与故事单元关联分析研究 被引量:3

Near Duplicate Keyframes Identifying and Correlation Analyzing of News Video Stories
下载PDF
导出
摘要 实现数据库中全部故事单元的相似度分析所面临的复杂性问题相当突出.提出了一种有效的方法来克服这些问题.首先,对限制相似关键帧识别速度的因素进行了研究,通过构建关联分析子数据库和精简局部关键点数量来提高分析速度.然后研究了层次化过滤方法,以提高相似关键帧识别效率.进一步研究了通过相似关键帧判断故事单元的直接关联关系和利用关联关系的传递性获得故事单元之间的间接关联关系的故事单元关联分析方法.最后,研究提出了利用相似关键帧信息的故事单元相似度计算方法.实验结果显示,该方法显著提高了匹配与关联分析的速度,并且具有较高的效率,计算所得故事单元相似度能够很好地贴近用户感官. The quadratic complexity required for measuring the similarity of news stories makes it intractable in large-volume news videos.In this paper,an effective method is proposed to find a way to solve the problems.First,small partitions from the corpus and prune local keypoint are selected to accelerate matching speed.Then,a hierarchical approach for identifying near duplicate keyframes is proposed.Furthermore,this paper presents a method to identity correlation of stories based on near duplicate keyframes and transitivity of correlations.Finally,a method for calculating the similarity of news stories is presented based on near duplicate keyframes.Experimental results show that this approach greatly speeds up the matching speed and improves the matching accuracy.The similarity of stories is closer to users sensory.
出处 《软件学报》 EI CSCD 北大核心 2010年第11期2971-2984,共14页 Journal of Software
基金 国家自然科学基金Nos.60802080 61002020 国家高技术研究发展计划(863)No.2006AA01Z319~~
关键词 新闻视频 故事单元 相似关键帧 关联分析 news video; story; near duplicate keyframe; correlation analysis;
  • 相关文献

参考文献5

二级参考文献47

  • 1于俊清,汤旸,周向东.基于主色特征识别的新闻视频口播帧[J].计算机工程与科学,2004,26(8):28-31. 被引量:3
  • 2[1]Rui, Y., Huang, T.S. A uniform framework for video browsing and retrieval. In: Bovik, A., ed. The Image and Video Processing Handbook. Academic Press, 2000. 705~715.
  • 3[2]Ngo, C.W., Pong, T.C., Zhang, H.J., et al. Motion-Based video representation for scene change detection. In: Proceedings of the ICPR 2000. Barcelona, Spain, 2000.
  • 4[3]Irani, M., Anandan, P. Video indexing based on mosaic representations. Proceedings of the IEEE, 1998,86:905~921.
  • 5[4]Zhao, L., Qi, W., Li, S.Z., et al. Key-Frame extraction and shot retrieval using nearest feature line (NFL). In: Proceedings of the International Workshop on Multimedia Information Retrieval, in Conjunction with ACM Multimedia Conference 2000. Los Angeles, USA, 2000.
  • 6[5]Hanjalic, A., Lagendijk, R.L., Biemond, J. Automated high-level movie segmentation for advanced video-retrieval systems. IEEE Transactions on Circuits and Systems for Video Technology, 1999,9(4):580~588.
  • 7[6]Corridoni, J.M., Bimbo, A.D. Structured representation and automatic indexing of movie information content. Pattern Recognition, 1998,31(12):2027~2045.
  • 8[7]Rui, Y., Huang, T.S., Mehrotra, S. Exploring video structure beyond the shots. In: Proceedings of the IEEE Conference on Multimedia Computing and Systems. 1998. 237~240.
  • 9[8]Kender, J.R., Yeo, B.L. Video scene segmentation via continuous video coherence. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition. 1998. 367~373.
  • 10[9]Ferman, A.M., Krishnamachari, S., Tekalp, A.M., et al. Group-of-Frames/pictures color histogram descriptors for multimedia applications. In: Proceedings of the ICIP 2000. 2000.

共引文献156

同被引文献37

  • 1Wang J Z, Jia L, Wiederhold G. Simplicity : semantics-sensitive integrated matching for picture libraries[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23 ( 9 ) : 947 963, 2001.
  • 2Lowe D G. Distinctive image features from scale invariant keypoints [J]. International Journal of Computer Vision, 2004, 60(2) : 90 -110.
  • 3Qin J, Yung N H C. Feature fusion within local region using localized maximum-margin learning for scene categories [J]. Pattern Recognition, 45:1671 - 1683, 2012.
  • 4Vasconcelos, N, Lippman A. A Bayesian framework for semantic content characterization [C]//Proceedings of International Conference on Computer Vision and Pattern Recognition, 23 -25 Jun 1998, Santa Barbara, CA, USA, 566 -571.
  • 5Wang J Z, Chen Y. Image categorization by learning and reasoning with Regions [J 1. Journal of Machine Learning Research, 2004:913-939.
  • 6Li J, Wang J Z. Automatic linguistic indexing of pictures by a statistical modeling approach [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25 (9): 1075 - 1088.
  • 7Murphy K, Torralba A, Freeman W. Using the forest to see the trees: A graphical model relating features, objects, and scenes [C]//Proceedings of Advances in Neural Information Processing Systems, Cambridge, 2004, MA : MIT Press.
  • 8Fergus F, Fei-Fei F, Perona P, et al. Learning objects categories from google' s image search [C]//Proceedings of 10^th IEEE International Conference on Computer Vision, 2005, 2:1816 - 1823.
  • 9Fei-Fei F, Perona P. A Bayesian hierarchical model for learning natural scene categories [C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2005,2:524 - 531.
  • 10Bosch A, Zisserman A, Munoz X. Scene classification via plsa[C]//Proceedings of European Conference on Computer Vision,2006, Graz, Austria, vol. 4:517 - 530.

引证文献3

二级引证文献4

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