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Multiple hypergraph ranking for video concept detection 被引量:1

Multiple hypergraph ranking for video concept detection
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摘要 This paper tackles the problem of video concept detection using the multi-modality fusion method. Motivated by multi-view learning algorithms, multi-modality features of videos can be represented by multiple graphs. And the graph-based semi-supervised learning methods can be extended to multiple graphs to predict the semantic labels for unlabeled video data. However, traditional graphs represent only homogeneous pairwise linking relations, and therefore the high-order correlations inherent in videos, such as high-order visual similarities, are ignored. In this paper we represent heterogeneous features by multiple hypergraphs and then the high-order correlated samples can be associated with hyperedges. Furthermore, the multi-hypergraph ranking (MHR) algorithm is proposed by defining Markov random walk on each hypergraph and then forming the mixture Markov chains so as to perform transductive learning in multiple hypergraphs. In experiments on the TRECVID dataset, a triple-hypergraph consisting of visual, textual features and multiple labeled tags is constructed to predict concept labels for unlabeled video shots by the MHR framework. Experimental results show that our approach is effective. This paper tackles the problem of video concept detection using the multi-modality fusion method. Motivated by multi-view learning algorithms, multi-modality features of videos can be represented by multiple graphs. And the graph-based semi-supervised learning methods can be extended to multiple graphs to predict the semantic labels for unlabeled video data. However, traditional graphs represent only homogeneous pairwise linking relations, and therefore the high-order correlations inherent in videos, such as high-order visual similarities, are ignored. In this paper we represent heterogeneous features by multiple hypergraphs and then the high-order correlated samples can be associated with hyperedges. Furthermore, the multi-hypergraph ranking (MHR) algorithm is proposed by defining Markov random walk on each hypergraph and then forming the mixture Markov chains so as to perform transductive learning in multiple hypergraphs. In experiments on the TRECVID dataset, a triple- hypergraph consisting of visual, textual features and multiple labeled tags is constructed to predict concept labels for unlabeled video shots by the MHR framework. Experimental results show that our approach is effective.
出处 《Journal of Zhejiang University-Science C(Computers and Electronics)》 SCIE EI 2010年第7期525-537,共13页 浙江大学学报C辑(计算机与电子(英文版)
基金 supported by the National Natural Science Foundation of China(Nos.60603096 and 60673088) the National High-Tech Re-search and Development Program(863)of China(No.2006AA010107) the Program for Changjiang Scholars and Innovative Research Team in University of China(No.IRT0652)
关键词 Multiple hypergraph ranking Video concept detection Multi-view learning Multiple labeled tags CLUSTERING Multiple hypergraph ranking, Video concept detection, Multi-view learning, Multiple labeled tags, Clustering
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  • 1Bickel,S.,Scheffer,T.,2004.Multi-View Clustering.Proc.4th IEEE Int.Conf.on Data Mining,p.19-26.[doi:10.1109/ICDM.2004.10095].
  • 2Dhillon,I.S.,2001.Co-clustering Documents and Words Using Bipartite Spectral Graph Partitioning.Proc.7th ACM SIGKDD Int.Conf.on Knowledge Discovery and Data Mining,p.269-274.[doi:10.1145/502512.502550].
  • 3Dumais,S.T.,Furnas,G.W.,Landauer,T.K.,1998.Using Latent Semantic Analysis to Improve Access to Textual Information.Proc.SIGCHI Conf.on Human Factors in Computing Systems,p.281-285.
  • 4Frey,B.J.,Dueck,D.,2007.Clustering by passing messages between data points.Science,315(5814):972-976.[doi:10.1126/science.1136800].
  • 5He,J.,Li,M.,Zhang,H.J.,Tong,H.H.,Zhang,C.S.,2004.Manifold-Ranking Based Image Retrieval.Proc.12th Annual ACM Int.Conf.on Multimedia,p.9-16.[doi:10.1145/1027527.1027531].
  • 6Hoi,S.C.H.,Lyu,M.R.,2008.A multimodal and multilevel ranking scheme for large-scale video retrieval.IEEE Trans.Multimedia,10(4):607-619.[doi:10.1109/TMM.2008.921735].
  • 7Liu,J.,Lai,W.,Hua,X.,Huang,Y.,Li,S.,2007.Video Search Re-ranking via Multi-Graph Propagation.Proc.15th Annual ACM Int.Conf.on Multimedia,p.208-217.[doi:10.1145/1291233.1291279].
  • 8Liu,Y.,Wu,F.,Zhuang,Y.,Xiao,J.,2008.Active PostRefined Multimodality Video Semantic Concept Detection with Tensor Representation.Proc.16th Annual ACM Int.Conf.on Multimedia,p.91-100.[doi:10.1145/1459359.1459372].
  • 9Long,B.,Yu,P.S.,Zhang,Z.F.,2008.A General Model for Multiple View Unsupervised Learning.Proc.SIAM Int.Conf.on Data Mining,p.822-833.
  • 10Naphade,M.,Smith,J.R.,Tesic,J.,Chang,S.F.,Hsu,W.,Kennedy,L.,Hauptmann,A.,Curtis,J.,2006.Large-scale concept ontology for multimedia.IEEE Multimedia,13(3):86-91.[doi:10.1109/MMUL.2006.63].

同被引文献11

  • 1AYERS D, SHAH M. Monitoring human behavior from video taken in an office environment[ J]. Image and Vision Computing, 2001, 19(12) : 833 - 846.
  • 2XU PENG, XIE LEXING, CHANG S-F, et al. Algorithms and systems for segmentation and structure analysis in soccer video[C]// IEEE International Conference on Multimedia and Expo. Washington, DC: IEEE Computer Society, 2001:721-724.
  • 3PERSE M, KRISTAN M, KOVACIC S, et al. A trajectory-based analysis of coordinated team activity in a basketball game[ J]. Computer Vision and Image Understanding, 2009, 113 (5) : 612 - 621.
  • 4FU Y W, GUO Y W, ZHU Y S, et al. Multi-view video summarization[J]. IEEE Transactions on Multimedia, 2010, 12(7): 717- 729.
  • 5ZHANG J W, WANG F, ZHANG C S, et al. Efficient multi-label classification with hyper-graph regularization[ C]//2009 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC: IEEE Computer Society, 2009:1658 - 1665.
  • 6LIU Q S, HUANG Y C, METAXAS D N. Hyper-graph with sampling for image retrieval[ J]. Pattern Recognition, 2011, 44 ( 10/ 11) : 2255 -2262.
  • 7LUO L, LI B, LI L, et al. Image analysis by discrete radial tchebichef moment[ C]// 2010 International Conference on Measuring Technology and Mechatronics Automation. Washington, DC: IEEE Computer Society, 2010:569-572.
  • 8HUANG D, SHAN C F, WANG Y H, et al. Local binary patterns and its application to facial image analysis: a survey [ J]. IEEE Transactions on Systems, Man, and Cybernetics-part C: Applications and Reviews, 2011, 41(6): 765-771.
  • 9张玉珍,魏带娣,王建宇,戴跃伟.基于多模态融合的足球视频语义分析[J].计算机科学,2010,37(7):273-276. 被引量:3
  • 10郑伟,王朝坤,刘璋,王建民.一种基于随机游走模型的多标签分类算法[J].计算机学报,2010,33(8):1418-1426. 被引量:57

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