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基于稀疏化图结构的转导多标注视频概念检测算法

, PP. 825-832

Keywords: 稀疏化描述,概念检测,多标注,半监督学习

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Abstract:

提出一种基于稀疏化图结构的转导多标注视频概念检测算法。首先,该方法通过信号稀疏化表达方法挖掘样本间视觉相似性关系与概念间分布相关性关系。然后,基于离散隐马尔可夫随机场构建多标注稀疏化图结构完成转导半监督视频概念检测。相关性信息的稀疏化表达可有效去除冗余信息的影响,降低图分类算法的问题复杂度,提高概念检测效率和分类效果。算法在TRECVID2005数据集上进行实验,并与多种有监督、半监督分类算法进行结果比较。实验结果验证该算法的有效性。

References

[1]  Zhu Xiaojin. Semi-Supervised Learning Literature Survey. Computer Sciences Technical Report, 1530. Madison, USA: University of Wisconsin, 2008
[2]  Zhu Xiaojin, Ghahramani Z, Lafferty J. Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions // Proc of the 20th International Conference on Machine Learning. Washington, USA, 2003: 912-919
[3]  Zhou Dengyong, Olivier B, Lal T N, et al. Learning with Local and Global Consistency // Thrun S, Saul L K, Schlkopf B, eds. Advances in Neural Information Processing Systems. Cambridge, USA: MIT Press, 2004, XVI: 321-328
[4]  Qi Guojun, Hua Xiansheng, Rui Yong, et al. Correlative Multi-Label Video Annotation // Proc of the 15th International Conference on Multimedia. Augsburg, Germany, 2007: 17-26
[5]  Chen Gang, Song Yanqiu, Wang Fei, et al. Semi-Supervised Multi-Label Learning by Solving a Sylvester Equation // Proc of the 8th SIAM Conference on Data Mining. Atlanta, USA, 2008: 410-419
[6]  Liu Yi, Jin Rong, Yang Liu. Semi-Supervised Multi-Label Learning by Constrained Non-Negative Matrix Factorization // Proc of the 21st National Conference on Artificial Intelligence. Saint Paul, USA, 2006, I: 421-426
[7]  Wang Jingdong, Zhao Yinghai, Wu Xiuqing, et al. Transductive Multi-Label Learning for Video Concept Detection // Proc of the 1st ACM International Conference on Multimedia Information Retrieval. Vancouver, Canada, 2008: 298-304
[8]  Roweis S T, Saul L K. Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science, 2000, 290(5500): 2323-2326
[9]  Wang Fei, Zhang Changshui. Label Propagation through Linear Neighborhoods // Proc of the 23rd International Conference on Machine Learning. Edinburgh, UK, 2006: 985-992
[10]  Rao R P N, Olshausen B A, Lewicki M S. Probabilistic Models of the Brain: Perception and Neural Function. Cambridge, USA: MIT Press, 2002
[11]  Wright J, Yang A, Ganesh A, et al. Robust Face Recognition via Sparse Representation. IEEE Trans on Pattern Analysis and Machine Intelligence, 2009, 31(2): 210-227
[12]  Tang Jinhui, Yan Shuicheng, Hong Richang, et al. Inferring Semantic Concepts from Community-Contributed Images and Noisy Tags // Proc of the 17th ACM International Conference on Multimedia. Beijing, China, 2009: 223-232
[13]  Liu Xiaobai, Cheng Bin, Yan Shuicheng, et al. Label to Region by Bi-Layer Sparsity Priors // Proc of the 17th ACM International Conference on Multimedia. Beijing, China, 2009: 115-124
[14]  Candes E, Rudelson M, Tao T, et al. Error Correction via Linear Programming // Proc of the 46th Annual IEEE Symposium on Foundations of Computer Science. Pittsburgh, USA, 2005: 295-308
[15]  Donoho D L. For Most Large Underdetermined Systems of Linear Equations the Minimal l1-Norm Solution is also the Sparsest Solution. Communications on Pure and Applied Mathematics, 2004, 59(7): 907-934
[16]  Vapnik V N. Statistical Learning Theory. New York, USA: Wiley, 1998
[17]  Boykov Y, Kolmogorov V. An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision. IEEE Trans on Pattern Analysis and Machine Intelligence, 2004, 26(9): 1124-1137
[18]  Kolmogorov V. Convergent Tree-Reweighted Message Passing for Energy Minimization. IEEE Trans on Pattern Analysis and Machine Intelligence, 2006, 28(10): 1568-1583
[19]  TRECVID 2005 [DB/OL]. [2010-03-05]. http://www-nlpir.nist.gov/projects/tv2005/tv2005.html
[20]  Trec-10 Proceedings Appendix on Common Evaluation Measures [EB/OL]. [2010-03-05]. http://trec.nist.gov/pubs/trec10/appendices/measures.pdf.
[21]  Naphade M, Smith J R, Tesic J, et al. Large-Scale Concept Ontology for Multimedia. IEEE MultiMedia, 2006, 13(3): 86-91
[22]  LSCOM Annotation [DB/OL]. [2010-03-05]. http://www.ee.columbia.edu/ln/dvmm/columbia374/
[23]  Zha Zhengjun, Mei Tao, Wang Jingdong, et al. Graph-Based Semi-Supervised Learning with Multi-Label // Proc of the IEEE International Conference on Multimedia and Exposition. Hannover, Germany, 2008: 1321-1324

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