Data Field Based Canonical Correlation Analysis and Its Application to Image Segmentation
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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.Abstract: In this paper, for extracting low-dimensional features from multi-dimensional data in data field environment, we propose a novel method of canonical correlation analysis(CCA) called DFCCA(data field based CCA) by introducing interactions among data into data correlation solving. The features extracted by DFCCA have better distribution properties, that is the features corresponding to a data point pair that are far apart from each other gather together in a small region, but other features corresponding to the pair of data points that are neighboring each other will scatter regularly around the region. Thanks to these properties, DFCCA has a good capability of frontier identification. Experimental results on image segmentation demonstrate that the frontiers extracted from complex images by DFCCA hold better fidelity.
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[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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