光照不变性特征在图像检索与识别中的应用研究
|
摘要
经过几十年的发展,图像检索与识别技术已经取得了一定的成果,并已广泛地应用于工业制造、金融、公安、司法、军事等领域。它们不仅速度快、效率高,而且还具有比人更胜一筹的准确度。图像检索与识别技术紧密联系,检索技术的发展进一步的推动了识别技术的发展,特别体现在手写体图像识别等应用当中。
光照问题是图像检索与识别面临的主要难题之一,由于光照变化改变了图像灰度空间的分布,影响图像检索的准确率,制约了图像识别系统的推广应用。本文在多尺度几何分析理论的基础上,主要研究光照不变性特征的提取与应用,进一步提高图像检索与识别的准确度,主要工作如下:
1针对离散小波变换不具备时移不变性、方向信息少的缺点,以及Contourlet变换存在频谱混叠的缺陷,提出基于抗混叠轮廓波变换与去噪模型结合提取光照不变性特征的图像检索改进算法。首先将图像转换到对数域,然后通过抗混叠轮廓波变换与去噪模型结合的方法提取光照不变性特征,最后采用K-最近特征线分类器进行分类。实验结果表明,改进算法的识别率比小波变换与去噪模型相结合方法提高5.48%,比基于形态学纹理特征的方法提高13.38%。
2根据金字塔对偶树方向滤波器组变换的相位信息具有光照不变性的特点,提出基于金字塔对偶树方向滤波器组变换提取光照不变性特征的图像检索算法。首先通过金字塔对偶树方向滤波器组变换和基于相对相位的Vonn模型得到图像的光照不变表示,然后利用提取的特征计算出RA距离,最后得到图像的检索率。实验结果表明,该算法比基于不变矩方法的查全率提高1.83%-13.42%,查准率提高10.76%-21.22%。
3将光照不变性特征相对相位应用于手写体文本图像识别。光照变化对手写体文本图像识别的影响虽然不是太明显,但是具有光照不变性的相对相位信息应用于识别当中能进一步改善识别系统的性能。首先通过金字塔对偶树方向滤波器组变换和基于幅度的DMD模型、基于相对相位的VM、WC分布模型得到图像的特征表示,然后利用提取特征运算出RA距离,最后根据多属性决策的思想,进行特征融合,并实现笔迹识别。实验结果表明,该方法的检索率最好能达到100%,优于现有的几种方法。
After several decades of development, technologies of image retrieval and recognition have made great achievements and been widely applied in many fields, such as industrial manufacturing, finance, public security, judicial branch and military. They are not only fast and highly efficient, but also have superior accuracies compared with human. There is a close relationship between image retrieval and recognition, the achievements of image retrieval technology further promote the advance of image recognition, which has been reflected in the application of handwriting recognition particularly.
Illumination condition is one of the main challenges in image retrieval and recognition. Because light changes the distribution of image in gray space, affects the accuracy of retrieval, limits the advanced promotion and application of image recognition system. Based on the theory of multiscale geometric analysis, the main research of this paper focuses on extraction and application of illuminant invariant feature, which will further improve the accuracies of image retrieval and recognition.
The main work is as follows:
①Considering the lack of time-shift invariance and directional information in discrete wavelet transform and the aliasing problem in Contourlet domain, we proposed a novel image retrieval algorithm of extracting illuminant invariant feature by combing non-aliasing Contourlet transform (NACT) and denoising model. First, the image is converted to the logarithmic domain, and then we extract illuminant invariant feature with the method of NACT and denoising model. Finally retrieval is completed by using K-nearest feature line classifier. Experimental results show that the recognition rate of improved algorithm is higher than that of discrete wavelet transform combined with denoising model by 5.48%, compared with methods based on morphological features texture increased 13.38%.
②According to the illumination invariance of phase information of the pyramidal dual-tree directional filter bank (PDTDFB) transform, an image retrieval algorithm of extracting illuminant invariant feature is proposed. First, we obtain a robust illumination feature of image using PDTDFB and relative phase depicted by Vonn distribution model. Then, the resistor-average(RA) distance is used to measure the similarity between images using the extracted features. Our experimental results show the effectiveness of the proposed algorithm. Compared with algorithm based on moment invariants, the recall rates improve by 1.83%-13.42%, precision rates increase by 10.76%-21.22%.
③An illuminant invariant feature based on relative phase information is applied in recognition of Chinese handwriting image. Although the light changes of handwriting image are not that obvious, relative phase information with illuminant invariance could further improve the performance of recognition systems. First, we obtain the feature description of image using PDTDFB, magnitude described by derived magnitude distribution (DMD) and relative phase which is depicted by Von Mises(VM) distribution and Wrapped Cauchy(WC) distribution model. Then we apply the RA distance to measure the similarity between images using the extracted features. Finally, motivated by the idea of multiple attribute decision making (MADM), the two kinds of features extracted from magnitude and relative phase are fused appropriately to realize writer identification. Experimental results show that our method performs better than other traditional methods, the accuracy reaches 100% in the best situation.
光照问题是图像检索与识别面临的主要难题之一,由于光照变化改变了图像灰度空间的分布,影响图像检索的准确率,制约了图像识别系统的推广应用。本文在多尺度几何分析理论的基础上,主要研究光照不变性特征的提取与应用,进一步提高图像检索与识别的准确度,主要工作如下:
1针对离散小波变换不具备时移不变性、方向信息少的缺点,以及Contourlet变换存在频谱混叠的缺陷,提出基于抗混叠轮廓波变换与去噪模型结合提取光照不变性特征的图像检索改进算法。首先将图像转换到对数域,然后通过抗混叠轮廓波变换与去噪模型结合的方法提取光照不变性特征,最后采用K-最近特征线分类器进行分类。实验结果表明,改进算法的识别率比小波变换与去噪模型相结合方法提高5.48%,比基于形态学纹理特征的方法提高13.38%。
2根据金字塔对偶树方向滤波器组变换的相位信息具有光照不变性的特点,提出基于金字塔对偶树方向滤波器组变换提取光照不变性特征的图像检索算法。首先通过金字塔对偶树方向滤波器组变换和基于相对相位的Vonn模型得到图像的光照不变表示,然后利用提取的特征计算出RA距离,最后得到图像的检索率。实验结果表明,该算法比基于不变矩方法的查全率提高1.83%-13.42%,查准率提高10.76%-21.22%。
3将光照不变性特征相对相位应用于手写体文本图像识别。光照变化对手写体文本图像识别的影响虽然不是太明显,但是具有光照不变性的相对相位信息应用于识别当中能进一步改善识别系统的性能。首先通过金字塔对偶树方向滤波器组变换和基于幅度的DMD模型、基于相对相位的VM、WC分布模型得到图像的特征表示,然后利用提取特征运算出RA距离,最后根据多属性决策的思想,进行特征融合,并实现笔迹识别。实验结果表明,该方法的检索率最好能达到100%,优于现有的几种方法。
After several decades of development, technologies of image retrieval and recognition have made great achievements and been widely applied in many fields, such as industrial manufacturing, finance, public security, judicial branch and military. They are not only fast and highly efficient, but also have superior accuracies compared with human. There is a close relationship between image retrieval and recognition, the achievements of image retrieval technology further promote the advance of image recognition, which has been reflected in the application of handwriting recognition particularly.
Illumination condition is one of the main challenges in image retrieval and recognition. Because light changes the distribution of image in gray space, affects the accuracy of retrieval, limits the advanced promotion and application of image recognition system. Based on the theory of multiscale geometric analysis, the main research of this paper focuses on extraction and application of illuminant invariant feature, which will further improve the accuracies of image retrieval and recognition.
The main work is as follows:
①Considering the lack of time-shift invariance and directional information in discrete wavelet transform and the aliasing problem in Contourlet domain, we proposed a novel image retrieval algorithm of extracting illuminant invariant feature by combing non-aliasing Contourlet transform (NACT) and denoising model. First, the image is converted to the logarithmic domain, and then we extract illuminant invariant feature with the method of NACT and denoising model. Finally retrieval is completed by using K-nearest feature line classifier. Experimental results show that the recognition rate of improved algorithm is higher than that of discrete wavelet transform combined with denoising model by 5.48%, compared with methods based on morphological features texture increased 13.38%.
②According to the illumination invariance of phase information of the pyramidal dual-tree directional filter bank (PDTDFB) transform, an image retrieval algorithm of extracting illuminant invariant feature is proposed. First, we obtain a robust illumination feature of image using PDTDFB and relative phase depicted by Vonn distribution model. Then, the resistor-average(RA) distance is used to measure the similarity between images using the extracted features. Our experimental results show the effectiveness of the proposed algorithm. Compared with algorithm based on moment invariants, the recall rates improve by 1.83%-13.42%, precision rates increase by 10.76%-21.22%.
③An illuminant invariant feature based on relative phase information is applied in recognition of Chinese handwriting image. Although the light changes of handwriting image are not that obvious, relative phase information with illuminant invariance could further improve the performance of recognition systems. First, we obtain the feature description of image using PDTDFB, magnitude described by derived magnitude distribution (DMD) and relative phase which is depicted by Von Mises(VM) distribution and Wrapped Cauchy(WC) distribution model. Then we apply the RA distance to measure the similarity between images using the extracted features. Finally, motivated by the idea of multiple attribute decision making (MADM), the two kinds of features extracted from magnitude and relative phase are fused appropriately to realize writer identification. Experimental results show that our method performs better than other traditional methods, the accuracy reaches 100% in the best situation.
引文
[1]贺玲,吴玲达,蔡益朝. CBIR中的索引技术综述[J].小型微型计算机系统, 2006, 27(1):141-145.
[2]耿苑.结合低层特征和高层语义的图像检索系统[D].西安:西北工业大学, 2004.
[3] Ma W Y,Zhang H J.Benchmarking of Image Features for Content-based Retrieval[C].The 32nd Asilomar Conference on Signals,Systems&Computers.Pacific Grove,California,USA:IEEE CS Press,1998.
[4]李向阳,庄越挺,潘云鹤.基于内容的图像检索技术与系统[J].计算机研究与发展,2001,38(3):344—354.
[5] Flicker, Metal. Query By Image Radio Content[M]. The QBIC System Computer, 1995.
[6]施智平,胡宏,李清勇等.基于纹理谱描述子的图像检索[J].软件学报,2005,16(6):1039-1045.
[7]章毓晋.图像处理和分析[M].北京:清华大学出版社,2001.
[8] Minh N Do, Martin Vetterli. The Contourlet Transform: An Efficient Directional Multiresolution Image Representation [J]. IEEE Transactions on Image Processing, 2005, 14 (12) : 2091～2106.
[9] Lu Y, Do M N. A new Contourlet Transform with Sharp Frequency Localization[A]. Proc. of IEEE International Conference on Image Processing[C]. Atlanta, USA, 2006, 2: 1629-1632.
[10]冯鹏,魏彪,米德伶等.基于抗混叠轮廓波变换系数分布模型的去噪算法研究[J].仪器仪表学报,2009,30(11):2362-2365.
[11]金炜,励金祥,周亚训.抗混叠轮廓波变换及其在图像融合中的应用[J].红外与毫米波学报, 2009,28(5):392-395.
[12]闫河,刘加伶,闫卫军等.基于去频谱混叠Contourlet变换的层内局部相关性图像降噪[J].电路与系统学报,2009,14(1):82-86.
[13] N.Kingsbury. The Dual Tree Complex Wavelet Transform: A New Efficient Tool for Image Restoration and Enhancement. In:Proceeding of EUSIPCO,1998:319-322.
[14] N.Kingsbury. Image Processing with Complex Wavelets. Philosophical Transactions: Mathematical, Physical and Engineering Sciences, 1999, 357: 2543-2560.
[15] N.Kingsbury. Shift Invariant Properties of the Dual-tree Complex Wavelet Transform[C]. In IEEE International Conference on Acoustics ,Speech ,and Signal, 1999 ,3:1221-1224.
[16] N.Kingsbury. The Dual-tree Complex Wavelet Transform: A New Technique for Shift Invariance and Directional filters. In:Proceedings of 8th IEEE Digital Signal ProcessingWorkshop.Bryce Canyou, utan,USA,1998:86-89.
[17] Truong T. Nguyen, Soontorn Oraintara. The Shiftable Complex Directional Pyramid, PartI: Theoretical Aspects[J]. IEEE Transactions on Signal Processing, 56(10): 4651-4660.
[18] Truong T. Nguyen, Soontorn Oraintara. The Shiftable Complex Directional Pyramid, PartII: Implementation and Applications[J]. IEEE Transactions on Signal Processing, 56(10): 4661-4672.
[19] T. Binford. Generic Surface Interpretation[C]. In Proc. of the 4th International System on Robotices Research, Santa Curz, CA, 1987 (8):55-57.
[20] T. R, Raviv, A. Shashua. The quotient Image: Class Based Recognition and Synthesis under Varying Illumination Conditions[C]. Proc.1999 IEEE Conf. on Computer Vison and Pattern Recognition (CVPR99),1999 (2):2566—2571.
[21] A.Shashua. On Photametric Issues in 3D Visual Recognition from a Single 2D Image[C]. Int. Journal of Computer Vision (IJCV),1997, 21(2):99-122.
[22] T Ojala, M Pietik?inen, Topi M?enp??.Multiresolution Gray Scale and Rotation Invariant Texture Analysis with Local Binary-Patterns[J]. Pattern Analysis and Machine Intelligence. 2002, 24(7):971—987
[23] D.J.Jobson, Z.Rahman and G.A.Woodel. Properties and Performance of A Center/Surround Retinex[C]. IEEE Trans. on image Processing: special issue on color processing,1996.
[24] D.J.Jobson, Z.Rahman and G.A.Woodel. A Multiscale Retinex for Bridging the Gap between Color Images and the Human Observation of Scences[J]. IEEE Trans. On Image Processing,6(7).
[25] Allan Hanbury, Umasankar Kandaswamy, Donald A. Adjeroh. Illumination-invariant Morphological Texture Classification[J]. Computational Imaging and Vision, 2005, Vol. 30: 377-386.
[26] Taiping Zhang , Bin Fang, Yuan Yuan,etc. Multiscale Facial Structure Representation for Face Recognition under Varying Illumination[J]. Pattern Recognition, 42 (2009):51– 258.
[27] A. Georghiades, P. BelhUmeur. From Few to Many: Illumination Cone Models far Face Recognition Under Variable Lighting and Pose[J]. IEEE Transactions of Pattern Analysis and Machine Intelligence, 2001, 23( 6):643-660.
[28] R.Basr I, D. Jacobs. Lambertian Reflectance and Linear Subspaces[C]. Proc.8th IEEE Int. Conf. on Computer Vision(ICCV01), 2001:383-390.
[29] R. Basr i, Subspaces.D. Jacohs. Lambertian Reflectance and Linear Subspaces[J]. IEEE Trans. On PAMI, 2003, 25 (2):218-233
[30] R.Ramaoorthi, P.Hanrahan. On the Relationship Between Radiance and Irradiance:Determining the Illumination from Images of a Convex Lambertian Object[J]. Journal of Optical Society of America A(JOSA), 2001,vol.18 No. 10:2448-2459.
[31] Xudong Xie, Kin-Man. A Efficient Illumination Normalization Method for Face Recognition[J]. Pattern Recognition Letters, 2006(27):609-617.
[32] S. D. Wei,S. H. Lai .Robust Face Recognition under Lighting Variations[C]. Proc.17thInt. Conf. on Pattern Recognition(ICPR04),2004(1):354-357.
[33] Nguyen T T, Oraintara S. The Multi-resolution Direction Filter Banks: Theory, Design and Applications[J]. IEEE Trans. on Signal Processing, 2005,53(10):3895-3905.
[34]焦李成,侯彪,王爽,刘芳.图像多尺度几何分析理论与应用[M].西安电子科技大学出版社,2008.
[35] Horn, B. K. P. Robot vision[M].Cambridge, MA: MIT Press,1986.
[36] S. Grace Chang, Bin Yu, Martin Vetterli. Adaptive Wavelet Thresholding for Image Denoising and Compression. [J] IEEE Transactions on image processing, 2000, 9(9):1532-1546.
[37] Li SZ, Lu JW. Face Recognition using the Nearest Feature Line Method[J]. IEEE Transactions Neural Networks, 1999,10(2):439-443.
[38] Zheng WM, Zhao L, Zou CR. Locally Nearest Neighbor Classifiers for Pattern Classification[J]. Pattern Recognition, 2004,37(6):1307-1309.
[39]刘华林,杨万麟,梅元媛等.修正最近特征分类器及其在雷达目标识别中的应用[J].计算机应用,2007,27(4):894-896.
[40] T. Ojala, T. M?enp??, M. Pietik?inen. Outex New Framework for Empirical Evaluation of Texture Analysis Algorithms. In Proceedings of the 16th ICPR, 2002,volume 1, pages 701-706.
[41]赵麟.基于颜色不变性的图像检索算法研究[D].北京:北京交通大学, 2009.
[42] Jinye Peng, Bianzhang Yu and Dakai Wang. Images Similarity Detection based on Directional Gradient Angular Histogram[C]. Proceedings of the16th International Conference on Pattern Recognition (ICPR2002), Québec City, vol.1, pp.147-150, August 2002.
[43]刘树棠译.信号与系统[M].西安.西安交通大学出版社,1998.315.
[44] An Vo and Soontorn Oraintara. A Study of Relative Phase in Complex Wavelet Domain: Property, Statistics and Applications in Texture Image Retrieval and Segmentation[J]. Signal Processing: Image Communication, 2010,25(1): 28-46.
[45] An Vo, Soontorn Oraintara and Nha Nguyen. Vonn Distribution of Relative Phase for Statistical Image Modeling in Complex Wavelet Domain[J]. Signal Processing, 2011, 91 (1):114-125.
[46] Don H. Johnson and Sinan Sinanovi′c. Symmetrizing the Kullback-Leibler Distance [EB/OL].[2000-03-28].http://www. ece.rice.edu/~dhj/resistor.pdf.
[47] Do M N, Vetteli M. Wavelet Based Texture Retrieval Using Generalized Gaussian Density and Kullback-Leibler Distance[J]. IEEE Trans. on Image Processing, 2002,11(2): 146-158.
[48] He Zhenyu, Tang Yuanyan, You Xinge. A Contourlet-based Method for Writer Identification[C]. Proc. of Conf. on Systems, Man and Cybernetics. Hawaii, USA: [s. n.], 2005.
[49] Xu Dayuan, Shang Zhaowei, Tang Yuanyang. Handwriting-based Writer Identification with Complex Wavelet[C]. Proc. of Conf. on Wavelet Analysis and Pattern Recognition. Hong Kong, China: [s. n.], 2008.
[50] Zhu Beibei, Shang Zhaowei, Zhang Feng, et al. Chinese Handwriting-based Writer Identification with PDTDFB Transform[C]. Proc. of Conf. on Wavelet Analysis and Pattern Recognition. Baoding, China: [s. n.], 2009.
[51] Rakvongthai Y, Oraintara S. Statistical Image Modeling with the Magntitude Probability Density Function of Complex Wavelet Coefficients[C]. Proc. of IEEE International Symposium on Circuits and Systems. [S. l.]: IEEE Press, 2009.
[52]许叶军.基于BP神经网络的交互式赋权法及其应用研究[D].南京:东南大学, 2004.
[53] He Z, You X, Tang Y Y. Handwriting-based personal identification[J]. Pattern Recognition and Artificial Intelligence, 2006, 20(2):209–225.
[54]朱贝贝.脱机中文手写体笔迹识别的研究[D].重庆:重庆大学, 2010.
[55]崔雪梅,孙才新,李新等.实小波与复小波变换对局部放电在线监测中提取信号特征的特点研究[J].电工技术学报, 2004,19(7):90-94.
[2]耿苑.结合低层特征和高层语义的图像检索系统[D].西安:西北工业大学, 2004.
[3] Ma W Y,Zhang H J.Benchmarking of Image Features for Content-based Retrieval[C].The 32nd Asilomar Conference on Signals,Systems&Computers.Pacific Grove,California,USA:IEEE CS Press,1998.
[4]李向阳,庄越挺,潘云鹤.基于内容的图像检索技术与系统[J].计算机研究与发展,2001,38(3):344—354.
[5] Flicker, Metal. Query By Image Radio Content[M]. The QBIC System Computer, 1995.
[6]施智平,胡宏,李清勇等.基于纹理谱描述子的图像检索[J].软件学报,2005,16(6):1039-1045.
[7]章毓晋.图像处理和分析[M].北京:清华大学出版社,2001.
[8] Minh N Do, Martin Vetterli. The Contourlet Transform: An Efficient Directional Multiresolution Image Representation [J]. IEEE Transactions on Image Processing, 2005, 14 (12) : 2091～2106.
[9] Lu Y, Do M N. A new Contourlet Transform with Sharp Frequency Localization[A]. Proc. of IEEE International Conference on Image Processing[C]. Atlanta, USA, 2006, 2: 1629-1632.
[10]冯鹏,魏彪,米德伶等.基于抗混叠轮廓波变换系数分布模型的去噪算法研究[J].仪器仪表学报,2009,30(11):2362-2365.
[11]金炜,励金祥,周亚训.抗混叠轮廓波变换及其在图像融合中的应用[J].红外与毫米波学报, 2009,28(5):392-395.
[12]闫河,刘加伶,闫卫军等.基于去频谱混叠Contourlet变换的层内局部相关性图像降噪[J].电路与系统学报,2009,14(1):82-86.
[13] N.Kingsbury. The Dual Tree Complex Wavelet Transform: A New Efficient Tool for Image Restoration and Enhancement. In:Proceeding of EUSIPCO,1998:319-322.
[14] N.Kingsbury. Image Processing with Complex Wavelets. Philosophical Transactions: Mathematical, Physical and Engineering Sciences, 1999, 357: 2543-2560.
[15] N.Kingsbury. Shift Invariant Properties of the Dual-tree Complex Wavelet Transform[C]. In IEEE International Conference on Acoustics ,Speech ,and Signal, 1999 ,3:1221-1224.
[16] N.Kingsbury. The Dual-tree Complex Wavelet Transform: A New Technique for Shift Invariance and Directional filters. In:Proceedings of 8th IEEE Digital Signal ProcessingWorkshop.Bryce Canyou, utan,USA,1998:86-89.
[17] Truong T. Nguyen, Soontorn Oraintara. The Shiftable Complex Directional Pyramid, PartI: Theoretical Aspects[J]. IEEE Transactions on Signal Processing, 56(10): 4651-4660.
[18] Truong T. Nguyen, Soontorn Oraintara. The Shiftable Complex Directional Pyramid, PartII: Implementation and Applications[J]. IEEE Transactions on Signal Processing, 56(10): 4661-4672.
[19] T. Binford. Generic Surface Interpretation[C]. In Proc. of the 4th International System on Robotices Research, Santa Curz, CA, 1987 (8):55-57.
[20] T. R, Raviv, A. Shashua. The quotient Image: Class Based Recognition and Synthesis under Varying Illumination Conditions[C]. Proc.1999 IEEE Conf. on Computer Vison and Pattern Recognition (CVPR99),1999 (2):2566—2571.
[21] A.Shashua. On Photametric Issues in 3D Visual Recognition from a Single 2D Image[C]. Int. Journal of Computer Vision (IJCV),1997, 21(2):99-122.
[22] T Ojala, M Pietik?inen, Topi M?enp??.Multiresolution Gray Scale and Rotation Invariant Texture Analysis with Local Binary-Patterns[J]. Pattern Analysis and Machine Intelligence. 2002, 24(7):971—987
[23] D.J.Jobson, Z.Rahman and G.A.Woodel. Properties and Performance of A Center/Surround Retinex[C]. IEEE Trans. on image Processing: special issue on color processing,1996.
[24] D.J.Jobson, Z.Rahman and G.A.Woodel. A Multiscale Retinex for Bridging the Gap between Color Images and the Human Observation of Scences[J]. IEEE Trans. On Image Processing,6(7).
[25] Allan Hanbury, Umasankar Kandaswamy, Donald A. Adjeroh. Illumination-invariant Morphological Texture Classification[J]. Computational Imaging and Vision, 2005, Vol. 30: 377-386.
[26] Taiping Zhang , Bin Fang, Yuan Yuan,etc. Multiscale Facial Structure Representation for Face Recognition under Varying Illumination[J]. Pattern Recognition, 42 (2009):51– 258.
[27] A. Georghiades, P. BelhUmeur. From Few to Many: Illumination Cone Models far Face Recognition Under Variable Lighting and Pose[J]. IEEE Transactions of Pattern Analysis and Machine Intelligence, 2001, 23( 6):643-660.
[28] R.Basr I, D. Jacobs. Lambertian Reflectance and Linear Subspaces[C]. Proc.8th IEEE Int. Conf. on Computer Vision(ICCV01), 2001:383-390.
[29] R. Basr i, Subspaces.D. Jacohs. Lambertian Reflectance and Linear Subspaces[J]. IEEE Trans. On PAMI, 2003, 25 (2):218-233
[30] R.Ramaoorthi, P.Hanrahan. On the Relationship Between Radiance and Irradiance:Determining the Illumination from Images of a Convex Lambertian Object[J]. Journal of Optical Society of America A(JOSA), 2001,vol.18 No. 10:2448-2459.
[31] Xudong Xie, Kin-Man. A Efficient Illumination Normalization Method for Face Recognition[J]. Pattern Recognition Letters, 2006(27):609-617.
[32] S. D. Wei,S. H. Lai .Robust Face Recognition under Lighting Variations[C]. Proc.17thInt. Conf. on Pattern Recognition(ICPR04),2004(1):354-357.
[33] Nguyen T T, Oraintara S. The Multi-resolution Direction Filter Banks: Theory, Design and Applications[J]. IEEE Trans. on Signal Processing, 2005,53(10):3895-3905.
[34]焦李成,侯彪,王爽,刘芳.图像多尺度几何分析理论与应用[M].西安电子科技大学出版社,2008.
[35] Horn, B. K. P. Robot vision[M].Cambridge, MA: MIT Press,1986.
[36] S. Grace Chang, Bin Yu, Martin Vetterli. Adaptive Wavelet Thresholding for Image Denoising and Compression. [J] IEEE Transactions on image processing, 2000, 9(9):1532-1546.
[37] Li SZ, Lu JW. Face Recognition using the Nearest Feature Line Method[J]. IEEE Transactions Neural Networks, 1999,10(2):439-443.
[38] Zheng WM, Zhao L, Zou CR. Locally Nearest Neighbor Classifiers for Pattern Classification[J]. Pattern Recognition, 2004,37(6):1307-1309.
[39]刘华林,杨万麟,梅元媛等.修正最近特征分类器及其在雷达目标识别中的应用[J].计算机应用,2007,27(4):894-896.
[40] T. Ojala, T. M?enp??, M. Pietik?inen. Outex New Framework for Empirical Evaluation of Texture Analysis Algorithms. In Proceedings of the 16th ICPR, 2002,volume 1, pages 701-706.
[41]赵麟.基于颜色不变性的图像检索算法研究[D].北京:北京交通大学, 2009.
[42] Jinye Peng, Bianzhang Yu and Dakai Wang. Images Similarity Detection based on Directional Gradient Angular Histogram[C]. Proceedings of the16th International Conference on Pattern Recognition (ICPR2002), Québec City, vol.1, pp.147-150, August 2002.
[43]刘树棠译.信号与系统[M].西安.西安交通大学出版社,1998.315.
[44] An Vo and Soontorn Oraintara. A Study of Relative Phase in Complex Wavelet Domain: Property, Statistics and Applications in Texture Image Retrieval and Segmentation[J]. Signal Processing: Image Communication, 2010,25(1): 28-46.
[45] An Vo, Soontorn Oraintara and Nha Nguyen. Vonn Distribution of Relative Phase for Statistical Image Modeling in Complex Wavelet Domain[J]. Signal Processing, 2011, 91 (1):114-125.
[46] Don H. Johnson and Sinan Sinanovi′c. Symmetrizing the Kullback-Leibler Distance [EB/OL].[2000-03-28].http://www. ece.rice.edu/~dhj/resistor.pdf.
[47] Do M N, Vetteli M. Wavelet Based Texture Retrieval Using Generalized Gaussian Density and Kullback-Leibler Distance[J]. IEEE Trans. on Image Processing, 2002,11(2): 146-158.
[48] He Zhenyu, Tang Yuanyan, You Xinge. A Contourlet-based Method for Writer Identification[C]. Proc. of Conf. on Systems, Man and Cybernetics. Hawaii, USA: [s. n.], 2005.
[49] Xu Dayuan, Shang Zhaowei, Tang Yuanyang. Handwriting-based Writer Identification with Complex Wavelet[C]. Proc. of Conf. on Wavelet Analysis and Pattern Recognition. Hong Kong, China: [s. n.], 2008.
[50] Zhu Beibei, Shang Zhaowei, Zhang Feng, et al. Chinese Handwriting-based Writer Identification with PDTDFB Transform[C]. Proc. of Conf. on Wavelet Analysis and Pattern Recognition. Baoding, China: [s. n.], 2009.
[51] Rakvongthai Y, Oraintara S. Statistical Image Modeling with the Magntitude Probability Density Function of Complex Wavelet Coefficients[C]. Proc. of IEEE International Symposium on Circuits and Systems. [S. l.]: IEEE Press, 2009.
[52]许叶军.基于BP神经网络的交互式赋权法及其应用研究[D].南京:东南大学, 2004.
[53] He Z, You X, Tang Y Y. Handwriting-based personal identification[J]. Pattern Recognition and Artificial Intelligence, 2006, 20(2):209–225.
[54]朱贝贝.脱机中文手写体笔迹识别的研究[D].重庆:重庆大学, 2010.
[55]崔雪梅,孙才新,李新等.实小波与复小波变换对局部放电在线监测中提取信号特征的特点研究[J].电工技术学报, 2004,19(7):90-94.