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数据场典型相关分析及其在图像分割中的应用

李文平 杨静 印桂生 张健沛

李文平, 杨静, 印桂生, 张健沛. 数据场典型相关分析及其在图像分割中的应用. 自动化学报, 2015, 41(4): 772-784. doi: 10.16383/j.aas.2015.c130896
引用本文: 李文平, 杨静, 印桂生, 张健沛. 数据场典型相关分析及其在图像分割中的应用. 自动化学报, 2015, 41(4): 772-784. doi: 10.16383/j.aas.2015.c130896
LI Wen-Ping, YANG Jing, YIN Gui-Sheng, ZHANG Jian-Pei. Data Field Based Canonical Correlation Analysis and Its Application to Image Segmentation. ACTA AUTOMATICA SINICA, 2015, 41(4): 772-784. doi: 10.16383/j.aas.2015.c130896
Citation: LI Wen-Ping, YANG Jing, YIN Gui-Sheng, ZHANG Jian-Pei. Data Field Based Canonical Correlation Analysis and Its Application to Image Segmentation. ACTA AUTOMATICA SINICA, 2015, 41(4): 772-784. doi: 10.16383/j.aas.2015.c130896

数据场典型相关分析及其在图像分割中的应用


DOI: 10.16383/j.aas.2015.c130896
详细信息
    作者简介:

    李文平 哈尔滨工程大学国家大学科技园博士后,嘉兴学院讲师.主要研究方向为数据挖掘,隐私保护,膜计算.E-mail:liwenping@hrbeu.edu.cn

    通讯作者: 杨静 哈尔滨工程大学教授.主要研究方向为数据库理论,数据挖掘,隐私保护.本文通信作者.E-mail:yangjing@hrbeu.edu.cn
  • 基金项目:

    国家自然科学基金(61370083,61073043,61073041,61402126),高等学校博士学科点专项科研基金(20112304110011,20122304110012)资助

Data Field Based Canonical Correlation Analysis and Its Application to Image Segmentation

More Information
  • Fund Project:

    Supported by National Natural Science Foundation of China(61370083, 61073043, 61073041, 61402126), and Research Fund for the Doctoral Program of Higher Education of China(20112304110011, 20122304110012)

  • 摘要: 针对数据场环境下多维数据的低维特征提取问题,本文将数据之间的相互作用纳入其相关性求解中,提出一种基于数据场的典型相关分析(Data field based canonical correlation analysis, DFCCA)方法. DFCCA提取的特征具有良好的分布特性,原空间上相隔较远的数据点对的特征聚集在一个较小区域内,而相邻数据点对的特征却有规律地分布在其他点所聚集区域的周围.此特性使得DFCCA具有较好的边界辨识能力,将其应用于图像分割的实验结果表明, DFCCA提取的复杂图像边界具有较好的保真度.
  • [1] Hotelling H. Relations between two sets of variates. Biometrika, 1936, 28(3):321-377
    [2] [2] Chaudhuri K, Kakade S M, Livescu K, Sridharan K. Multi-view clustering via canonical correlation analysis. In:Proceedings of the 26th International Conference on Machine Learning. Montreal, Canada:ICML, 2009. 129-136
    [3] [3] Olcay K, Ethem A, Oleg V F. Canonical correlation analysis using within-class coupling. Pattern Recognition Letters, 2011, 32(2):134-144
    [4] Peng Yan, Zhang Dao-Qiang. Semi-supervised canonical correlation analysis algorithm. Journal of Software, 2008, 19(11):2822-2832(彭岩, 张道强. 半监督典型相关分析算法. 软件学报, 2008, 19(11):2822-2832)
    [5] Sun Quan-Sen, Zeng Sheng-Gen, Heng Pheng-Ann, Xia De-Shen. The theory of canonical correlation analysis and its application to feature fusion. Chinese Journal of Computers, 2005, 28(9):1524-1533(孙权森, 曾生根, 王平安, 夏德深. 典型相关分析的理论及其在特征融合中的应用. 计算机学报, 2005, 28(9):1524-1533)
    [6] Hou Shu-Dong, Sun Quan-Sen. Sparsity preserving canonical correlation analysis with application in feature fusion. Acta Automatica Sinica, 2012, 38(4):659-665(侯书东, 孙权森. 稀疏保持典型相关分析及在特征融合中的应用. 自动化学报, 2012, 38(4):659-665)
    [7] [7] Huang H, He H T, Fan X, Zhang J P. Super-resolution of human face image using canonical correlation analysis. Pattern Recognition, 2010, 43(7):2532-2543
    [8] [8] Jia C C, Wang S J, Peng X J, Pang W, Zhang C Y, Zhou C G, Yu Z Z. Incremental multi-linear discriminant analysis using canonical correlations for action recognition. Neurocomputing, 2012, 83:56-63
    [9] Hong Quan, Chen Song-Can, Ni Xue-Lei. Sub-pattern canonical correlation analysis with application in face recognition. Acta Automatica Sinica, 2008, 34(1):21-30(洪泉, 陈松灿, 倪雪蕾. 子模式典型相关分析及其在人脸识别中的应用. 自动化学报, 2008, 34(1):21-30)
    [10] Yuan Y H, Sun Q S, Ge H W. Fractional-order embedding canonical correlation analysis and its applications to multi-view dimensionality reduction and recognition. Pattern Recognition, 2014, 47(3):1411-1424
    [11] An B G, Guo J H, Wang H S. Multivariate regression shrinkage and selection by canonical correlation analysis. Computational Statistics Data Analysis, 2013, 62(6):93-107
    [12] Singh A, Kulkarni M A, Mohanty U C, Kar S C, Robertson A W, Mishra G. Prediction of Indian summer monsoon rainfall(ISMR) using canonical correlation analysis of global circulation model products. Meteorological Applications, 2012, 19(2):179-188
    [13] Fukumizu K, Bach F R, Gretton A. Statistical consistency of kernel canonical correlation analysis. Journal of Machine Learning Research, 2007, 8(2):361-383
    [14] Zheng W M, Zhou X Y, Zou C R, Zhao L. Facial expression recognition using kernel canonical correlation analysis(KCCA). IEEE Transactions on Neural Networks, 2006, 17(1):233-238
    [15] Hardoon D R, Mouro Miranda J, Brammer M, Taylor J S. Unsupervised analysis of fMRI data using kernel canonical correlation. Neuroimage, 2007, 37(4):1250-1259
    [16] Zhu X F, Huang Z, Shen H T, Cheng J, Xu C S. Dimensionality reduction by mixed kernel canonical correlation analysis. Pattern Recognition, 2012, 45(8):3003-3016
    [17] Shu C, Ouarda T B M J. Flood frequency analysis at ungauged sites using artificial neural networks in canonical correlation analysis physiographic space. Water Resources Research, 2007, 43(7), doi: 10.1029/2006WR005142
    [18] Karageorgiou E, Lewis S M, Mccarten J R, Leuthold A C, Hemmy L S, Mcpherson S E, Rottunda S J, Rubins D M, Georgopoulos A P. Canonical correlation analysis of synchronous neural interactions and cognitive deficits in Alzheimer's dementia. Journal of Neural Engineering, 2012, 9(5):056003
    [19] Gu Jing-Jing, Chen Song-Can, Zhuang Yi. Localization in wireless sensor network using locality preserving canonical correlation analysis. Journal of Software, 2010, 21(11):2883-2891(顾晶晶, 陈松灿, 庄毅. 用局部保持典型相关分析定位无线传感器网络节点. 软件学报, 2010, 21(11):2883-2891)
    [20] Wang F S, Zhang D Q. A new locality-preserving canonical correlation analysis algorithm for multi-view dimensionality reduction. Neural Processing Letters, 2013, 37(2):135-146
    [21] Yuan Y H, Sun Q S. Graph regularized multiset canonical correlations with applications to joint feature extraction. Pattern Recognition, 2014, 47(12):3907-3919
    [22] Gomez D D, Maletti G, Nielsen A A, Ersboll B. Multiset multitemporal canonical analysis of psoriasis images. In:Proceedings of the 2004 IEEE International Symposium on Biomedical Imaging. Washington D.C., USA:IEEE, 2004. 1151-1154
    [23] Thompson B, Cartmill J, Azimi S M R, Schock S G. A multichannel canonical correlation analysis feature extraction with application to buried underwater target classification. In:Proceedings of the 2006 IEEE International Joint Conference on Neural Network. Vancouver, Canada:IEEE, 2006. 4413-4420
    [24] Li Y O, Adali T, Wang W, Calhoun V D. Joint blind source separation by multiset canonical correlation analysis. IEEE Transactions on Signal Processing, 2009, 57(10):3918-3929
    [25] Yuan Y H, Sun Q S. Fractional-order embedding multiset canonical correlations with applications to multi-feature fusion and recognition. Neurocomputing. 2013, 122(12):229-238
    [26] Deleus F, Van Hulle M M. Functional connectivity analysis of fMRI data based on regularized multiset canonical correlation analysis. Journal of Neuroscience Methods, 2011, 197(1):143-157
    [27] Yang Jing, Li Wen-Ping, Zhang Jian-Pei. Canonical correlation analysis of big data based on cloud model. Journal of Communications, 2013, 34(10):121-134(杨静, 李文平, 张健沛. 大数据典型相关分析的云模型方法. 通信学报, 2013, 34(10):121-134)
    [28] Sun L A, Ji S W, Ye J P. Canonical correlation analysis for multilabel classification:a least-squares formulation, extensions, and analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(1):194-204
    [29] Yang Xue-Mei, Dong Yi-Sheng, Xu Hong-Bing, Liu Xue-Jun, Qian Jiang-Bo, Wang Yong-Li. Online correlation analysis for multiple dimensions data streams. Journal of Computer Research and Development, 2006, 43(10):1744-1750(杨雪梅, 董逸生, 徐宏炳, 刘学军, 钱江波, 王永利. 高维数据流的在线相关性分析. 计算机研究与发展, 2006, 43(10):1744-1750)
    [30] Wang Yong-Li, Xu Hong-Bing, Dong Yi-Sheng, Qian Jiang-Bo, Liu Xue-Jun. A correlation analysis algorithm based on low-rank approximation for multiple dimension data streams. Acta Electronica Sinica, 2006, 34(2):293-300(王永利, 徐宏炳, 董逸生, 钱江波, 刘学军. 基于低阶近似的多维数据流相关性分析. 电子学报, 2006, 34(2):293-300)
    [31] Wang Y L, Zhang G X, Qian J B. ApproxCCA:an approximate correlation analysis algorithm for multidimensional data streams. Knowledge-Based Systems, 2011, 24(7):952-962
    [32] Kim M. Correlation-based incremental visual tracking. Pattern Recognition, 2012, 45(3):1050-1060
    [33] Zhou Yong, Lu Xiao-Wei, Cheng Chun-Tian. Parallel computing method of canonical correlation analysis for high-dimensional data streams in irregular streams. Journal of Software, 2012, 23(5):1053-1072(周勇, 卢晓伟, 程春田. 非规则流中高维数据流典型相关性分析并行计算方法. 软件学报, 2012, 23(5):1053-1072)
    [34] Yang Jing, Li Wen-Ping, Zhang Jian-Pei. A tracking algorithm based on rank two modifications for canonical correlation analysis of multidimensional data streams. Acta Electronica Sinica, 2012, 40(9):1765-1774(杨静, 李文平, 张健沛. 基于秩2 更新的多维数据流典型相关跟踪算法. 电子学报, 2012, 40(9):1765-1774)
    [35] Li De-Yi, Du Yi. Artificial Intelligence with Uncertainty. Beijing:National Defence Industry Press, 2005. 224-227(李德毅, 杜鷁. 不确定性人工智能. 北京:国防工业出版社, 2005. 224-227)
    [36] Milyaev S, Barinova O. Learning graph Laplacian for image segmentation. Transactions on Computational Science XIX, 2013, 7870:92-106
    [37] Sun J. Image edge detection based on relative degree of grey incidence and Sobel operator. Artificial Intelligence and Computational Intelligence, 2012, 7530:762-768
    [38] Klette R. Image Segmentation. London:Springer Press, 2014. 167-214
    [39] He C J, Wang Y, Chen Q. Active contours driven by weighted region-scalable fitting energy based on local entropy. Signal Processing, 2012, 92(2):587-600
    [40] Cho M, Mulee M K. Authority-shift clustering:hierarchical clustering by authority seeking on graphs. In:Proceedings of the 33th IEEE Conference on Computer Vision and Pattern Recognition. San Francisco, USA:IEEE, 2010. 3193-3200
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出版历程
  • 收稿日期:  2013-09-16
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  • 刊出日期:  2015-04-20

数据场典型相关分析及其在图像分割中的应用

doi: 10.16383/j.aas.2015.c130896
    基金项目:

    国家自然科学基金(61370083,61073043,61073041,61402126),高等学校博士学科点专项科研基金(20112304110011,20122304110012)资助

    作者简介:

    李文平 哈尔滨工程大学国家大学科技园博士后,嘉兴学院讲师.主要研究方向为数据挖掘,隐私保护,膜计算.E-mail:liwenping@hrbeu.edu.cn

    通讯作者: 杨静 哈尔滨工程大学教授.主要研究方向为数据库理论,数据挖掘,隐私保护.本文通信作者.E-mail:yangjing@hrbeu.edu.cn

摘要: 针对数据场环境下多维数据的低维特征提取问题,本文将数据之间的相互作用纳入其相关性求解中,提出一种基于数据场的典型相关分析(Data field based canonical correlation analysis, DFCCA)方法. DFCCA提取的特征具有良好的分布特性,原空间上相隔较远的数据点对的特征聚集在一个较小区域内,而相邻数据点对的特征却有规律地分布在其他点所聚集区域的周围.此特性使得DFCCA具有较好的边界辨识能力,将其应用于图像分割的实验结果表明, DFCCA提取的复杂图像边界具有较好的保真度.

English Abstract

李文平, 杨静, 印桂生, 张健沛. 数据场典型相关分析及其在图像分割中的应用. 自动化学报, 2015, 41(4): 772-784. doi: 10.16383/j.aas.2015.c130896
引用本文: 李文平, 杨静, 印桂生, 张健沛. 数据场典型相关分析及其在图像分割中的应用. 自动化学报, 2015, 41(4): 772-784. doi: 10.16383/j.aas.2015.c130896
LI Wen-Ping, YANG Jing, YIN Gui-Sheng, ZHANG Jian-Pei. Data Field Based Canonical Correlation Analysis and Its Application to Image Segmentation. ACTA AUTOMATICA SINICA, 2015, 41(4): 772-784. doi: 10.16383/j.aas.2015.c130896
Citation: LI Wen-Ping, YANG Jing, YIN Gui-Sheng, ZHANG Jian-Pei. Data Field Based Canonical Correlation Analysis and Its Application to Image Segmentation. ACTA AUTOMATICA SINICA, 2015, 41(4): 772-784. doi: 10.16383/j.aas.2015.c130896
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