摘要
随着数字信息技术的发展及广泛应用,数字图像和数字视频的数量增长迅速。图像分类任务正是在这样的一个前提下提出并发展起来。图像分类研究任务主要由预处理,特征提取和分类三个主要环节构成,每个环节对图像的分类效果都有重要的影响。本文从这一着眼点出发,对图像分类各环节的关键技术进行逐一分析,针对各个环节的处理任务,提出以下方法:
(1)针对光照对图像分类的影响,提出了自动截断拉伸的快速多尺度Retinex方法(TWMSR)。通过自动截断拉伸处理修正了多尺度Retinex方法(MSR)从log空间映射回灰度空间后,拉伸受少数极值点影响而造成的失真;提出一种窗口无关快速均值滤波算法,用于替代MSR方法中的高斯环境函数,提高了MSR方法的运算速度。TWMSR方法与多种亮度归一化和彩色常化方法进行去光照对比实验,证实该方法在亮度归一和彩色常化上具有最佳性能。
(2)为了去除噪声对图像分类的影响,提出梯度均变双边滤波图像去噪(GSBF)方法。通过分析图像的构成特性提出了图像均质判定规则,构建了梯度均变去噪方法;通过分析梯度均变方法和双边滤波方法性能,提出GSBF方法,实现了去噪和细节保留的平衡。将GSBF方法与多种去噪方法从主、客观的角度和特征稳定性角度进行对比实验,证实了GSBF方法能更有效地去除噪声,提高特征的稳定性。
(3)在特征区域获取环节,提出最大分布熵多尺度小波显著特征区域获取方法。通过采用最大分布熵确定待选取的小波特征点数量,有效地控制了特征的分布性;通过引入多尺度Log空间,实现了不同尺度小波特征区域的获取。将文中提出的小波区域获取方法与其它特征区域求取方法进行对比实验,从尺度、模糊、旋转、光照、视角五种图像特性变化的角度,根据重复性标准进行了比较,结合对这些算法所求得特征区域的相关性评价,提出联合特征区域求取方法。通过实验证明,该联合特征区域求取方法满足了特征区域提取的多样性。
(4)通过对不同图像特征描述子的描述特性分析,构建了一种联合特征描述子,该联合特征描述子由具有信息互补性的4种描述子构成。针对联合特征描述子的特性,提出一种基于打分制的Recall-precision特征描述子评价方法,将该联合描述子与其它描述子从图像特性改变的角度进行了实验比较,证实该联合描述子能够更稳定的描述图像特征区域。通过对联合描述子进行性能实验分析,确定了联合描述子的融合系数。
(5)分析视觉字集在图像分类中的应用,提出一种改进的K均值聚类方法,并用其生成视觉字集。结合概率拉丁语义模型(pLSA)和基于高斯混合贝叶斯两种分类训练模型,对文中所讨论的算法进行综合实验。实验表明,这些环节的改进有效的改善了图像分类效果,也进一步证实了各环节算法在相应图像处理功能上的有效性。
Along with the development and abroad application of digital information aquiring techniques, the number of digital images and digital videos has grown enormously. Image classification task is developed under this kind of background and composed of image preprocessing, feature extraction and classifying processing three steps. Each step has some important affects on the final classification results. This paper just focuses on this side, analyzes the key techniques of those steps in image classification in turn. And effect methods have been proposed with each step’s processing tasks:
(1)Aim at the light impact for image classification, we propose an automatic truncation stretch and fast multi-scale Retinex method (TWMSR). Through automatic truncation stretch method, repair the distortion of MSR caused by max or min pixels during the process of mapping from log space to gray space; propose a fast window size irrelevant mean filtering method to substitute the Gaussian environment function and improve the computing speed of MSR. Comparing the TWMSR method with several other illumination normalizing and color constancy methods and prove that TWMSR method has better performance in illumination normalizing and color constancy.
(2) For the popuse of getting rid of the noise impact for image classification, a gradient symmetrically changing bilateral filtering de-noising method (GSBF) is proposed. By analyzing the characteristics of image composing we propose the decision rules of symmetrically changing of image; combining the gradient symmetrically changing with bilateral filtering method. And then rebuild the filtered image iteratively using rules of symmetrically changing. Compare the GSBF method with some other de-noising methods, and give an analyzing on the stability of features. It proves that GSBF method can remove the noise more effectively and can get more stable features than others.
(3)Aim at feature region capturing, a max distributing entropy wavelet-based salient multi-scale region detector (WSMR) has been proposed. It controls the features distributing characteristics efficiently by using max distributing entropy; by introducing multi-scale log space it can get wavelet feature regions with different scales. Evaluate the proposed WSMR and some other feature region detection methods on scale, blur, rotation, light and view angle. Use repeatability criterion to compare those methods and combine them with relative criterion between those features detected by those methods, bring forward the unite feature region detection method. By experiment it has shown that the unite feature region detection method fulfills the diversity of feature regions.
(4)Through the analyzing on the expressed information carried by different descriptors, united descriptor method is constructed with several discriptors which have supplement informations for each other. Aiming at the characteristic of united descriptor, a Recall-precision evaluating method based on score rules is proposed. Compare the unite descriptor with some other descriptors and prove that the united descriptor can give the feature region a more stable expression. Further more, according to the comparing of chosen descriptors on several angles of image characteristics changing, the weighted coefficients of unite descriptors are decided.
(5)By alalyzing the principle of visual words in image classification, an improved K-means clustering algorithm is proposed to generate visual words. Then give the algorithms analyzing of probabilistic latent semantic analysis (pLSA) model and Bayesian decision classification model based on Gaussian mixture model. By Experiment on those algorithms discussed in the paper and concluded that the improvement in each step can bring up the image classification effect and can also make better performance in corresponding image processing function.
引文
[1] Anna Bosch, Andrew Zisserman, Xavier Muoz.. Scene classification using a hybrid generative/discriminative approach. IEEE transactions on pattern analysis and machine intelligence. 2008, 30(4):712-727P
[2]任建峰,郭雷,李刚.多类支持向量机的自然图像分类.西北工业大学学报.2005,23(3):295-298页
[3]谢昭.图像理解的关键问题和方法研究.合肥工业大学博士论文.2007
[4] Hong L, Wang Y, Jain A. Finger print image enhancement: algorithm and performance evaluation. TEEE Pattern Analysis and Machine Intelligence. 1998, 20(8): 777-789P
[5]王修信,胡维平,梁冬冬.小波变换在医学图像增强中的应用.中国医学物理学杂志.2002,19(2):127-128页
[6]岳昔娟,张勇,黄国满.改进的直方图均衡化在遥感图像分类中的应用.四川测绘.2008,31(4):158-161页
[7] Tinku Acharya, Ajoy K. Ray. Image Processing: Principles and Applications. Wiley-Interscience, 1-th edition, 2005
[8]刘红,沈利明,乐建威.X光图像增强处理研究.科学技术与工程.2007,7(22):5763-5766页
[9] S.Pizer. Adaptive histogram equalization and its variation. Computer Vision Graphics & Image Processing. 1987, 39(3): 355-368P
[10] Paranjape P.B.. Adaptive-neighborhood histogram equalization for image enhancement. CVGIP. 1992, 54(3): 259-267P
[11]杨词银,黄廉卿.基于幂函数的加权自适应直方图均衡.光电子激光.2002,13(5):515-517页
[12]魏先民.自适应直方图均衡方法研究与应用.信息技术与信息化.2005, 4:123-125页
[13] John B Zimmerman , Stephen M Pizer. An evaluation of the effectiveness of adaptive histogram equalization for contrast enhancement. IEEE Trans. On Medical Imaging. 1998 , 7(4): 304-312P
[14]张长江,汪晓东,张浩然.红外图像全局和局部对比度增强的非线性增益法.计算机辅助设计与图形学学报.2006,18(6):844-848页
[15]宋刚,刘瑶华.一种强化细节的自适应直方图均衡法.山东工业大学学报.1999,29(1):81-85页
[16]杨晖,翟丽荣.X线医学图像的对比度增强方法与实现.辽宁大学学报.2009,36(1):64-66页
[17]尚晋,杨有,李晓红.一种改进的自适应直方图均衡化增强档案图像的方法.计算机科学.2007,34(5):237-239页
[18] Thomas Deselaers, Daniel Keysers, Hermann Ney. Improving a discriminative approach to object recognition using image patches. In DAGM 2005, Pattern Recognition, Vienna, Austria, 2005, 326-333P
[19]边肇祺,张学工等.模式识别.北京,清华大学出版社,2000
[20]曾鹏鑫,么健石,陈鹏,徐心和.基于小波变换的图像增强算法.东北大学学报.2005,26(6):527-530页
[21] Mao-Yu Huang, Din-Chang Tseng, Liu M.S.C.. Wavelet image enhancement based on Teager energy operator. Proceedings of International Conference on Pattern Recognition. Quebec, Canada. 2002, 2: 993-996P
[22] S.Du, R.Ward. Wavelet-Based Illumination Normalization for Face Recognition. Proc. of IEEE International Conference on Image Processing. Genoa, Italy. 2005, 2: 954-957P
[23]吴军,田小林,孙延奎,唐泽圣.一种新的基于小波变换的自适应MRI增强算法.计算机应用研究.2008,25(6):1771-1775页
[24] H.Liu, W.Gao, J.Miao, D.Zhao, G.Deng, J.Li. Illumination Compensation and Feedback of Illumination Feature in Face Detection. Proc. IEEE International Conferences on Info-tech and Info-net. Beijing, China. 2001, 23: 444-449P
[25] K. Barnard, V. Cardei, and B.V. Funt. A comparison of computational color constancy algorithms-part i: Methodology and experiments with synthesized data. IEEE transactions on Image Processin. 2002, 11(9): 972-984P
[26] G. Buchsbaum. A spatial processor model for object colour perception. Journal of the Franklin Institute. 1980, 310: 1-26P.
[27] G.D. Finlayson, E. Trezzi. Shades of gray and colour constancy. In IS&T/SID Twelfth Color Imaging Conference. Scottsdale, Arizona. 2004, 37-41P
[28] J. van deWeijer , T. Gevers. Color constancy based on the grey-edge hypothesis. In ICIP. Genova, Italy. 2005, 722-725P
[29] Arjan Gijsenij. Theo Gevers. Color constancy by local averaging. International conference on image analysis and processing workshop. Modena, Italy. 2007, 171-174P
[30] G. Finlayson, S. Hordley, and I. Tastl. Gamut constrained程研究.2008,27(2):111-113页
[45]陈彦.巴特沃斯高通滤波器在图像处理中的应用.邵阳学院学报.2007,4(2):47-50页
[46] Unser M., Aldroubi A., Laine, A.. Special Issue on Wavelets in Medical Imaging. IEEE Transactions on Medical Imaging. 2003, 22(3): 285-288P
[47] Mallat S., Hwang WL. Singularity detection and processing with wavelet. IEEE Transactions on Information Theory. 1992, 38(2): 617-643P
[48] Y Xu. Wavelet transform domain filters, A spatially selective noise filtration tech-nique. IEEE Trans. on IP. 1994, (3): 217-237P
[49] Chang S G, Yu B., Vetterli M.. Adaptive wavelet thresholding for images denoising and compression. IEEE Transactions on image processing. 2000, 9(9): 1522-1531P
[50] Y. Jin, E.D. Angelini, A.F. Laine. Wavelets in Medical Image Processing: Denoising, Segmentation, and Registration, Handbook of Medical Image Analysis: Advanced Segmentation and Registration Models. edited by Jasjit Suri, David L. Wilson and Swamy Laximinarayan. Kluwer Academic Publishers, New York, NY, 2004
[51] Antoniadis A., Fan J.. Regularization of Wavelet Approximations. Journal of American Statistics Association. 2001, 96(455): 939-967P
[52]沈小平.正交小波展开的Gibbs现象.数学研究.2002M35(4):343-357页
[53]芮挺,王金岩,沈春林,丁健.基于PCA的图像小波去噪方法.小型微型计算机系统.2006,27(1):158-161页
[54]张旭,陈树越.一种基于统计特性的邻域均值滤波算法.科技情报开发与经济.2005,15(2):146-147页
[55] K.I.Diamantars, S.Y.Kung. Principal Component analysis networks. New York, 1996
[56]陆波,毕笃彦,谭军.一种基于KPCA的图像去噪方法.红外技术.2006,26(6):58-60页
[57] Takiquchi T., Ariki Y., Robust feature extraction using kernel PCA. International conference on speech and signal processing. Guilin, China. 2006, 1: 14-19P
[58] Dorin Comaniciu, P Meer. Mean Shift: a robust approach towardfeature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2002, 24(5): 603-619P
[59]王科俊,郭庆昌.基于均值移动算法的图像平滑.哈尔滨工程大学学报.2007,28(11):1228-1235页
[60] Buades A, Coll B, Morel J M. A non-local algorithm for image denoising. In Proc. IEEE Conference on Computer Vision and Pattern Recognition. San Diego, USA. 2005, 2: 60–65P
[61] Wang Kejun, Ren Zhen. Enhanced Gaussian mixture models for object recognition using salient image features. IEEE International conference on mechatronics and Automation. Harbin, China. 2007, 1229-1233P
[62] Rapha?l Marée, Pierre Geurts, Louis Wehenkel. Content-based image retrieval by indexing random subwindows with randomized trees. Asian comference on computer vision. Tokyo, Japan. 2007, 4844: 611-620P
[63] Ville Viitaniemi, Jorma Laaksonen. Technique for image classification, object detection and object segmentation. 10th International Conference. Visual Salerno, Italy. 2008, 126-137P
[64] Maree R., Geurts P., Piater J., Wehenkel L.. Random Subwindows for Robust Image Classification. IEEE conference on computer vision and pattern recognition. San Diego, USA. 2005, 1: 20-25P
[65] J. Winn, A. Criminisi, T. Minka. Object Categorization by Learned Universal Visual Dictionary. International Conference on Computer Vision. Beijing, China.2005, 2: 1800-1807P
[66] Jaesik Choi, Won J. Jeon, and Sang-Chul Lee. Spatio-temporal pyramid matching for sports videos. In Proceedings of ACM International Conference on Multimedia Information Retrieval. Vancouver, Canada. 2008, 291-297P
[67] C. Harris and M.J. Stephens. A combined corner and edge detector. In Alvey Vision Conference. 1988, 147-152P
[68] C. Schmid, R. Mohr, C. Bauckhage. Comparing and evaluating interest points. International Conference on Computer Vision. Bombay, India. 1998, 230-235P
[69] Mikolajczyk K., Schmid C.. Indexing based on scale invariant interest points. Proc. Int. Conf. Computer Vision. Vancouver, Canada. 2001, 525-531P
[70]张小洪,李博,杨丹.一种新的Harris多尺度角点检测.电子与信息学报.2007,29(7):1735-1738页
[71]高健,黄心汉,彭刚,王敏,吴祖玉.基于Harris角点和高斯差分的特征点提取算法.模式识别与人工智能.2008,21(2):171-176页
[72] Svetlana Lazebnik, Cordelia Schmid, Jean Ponce. Affine-Invariant Local Descriptors and Neighborhood Statistics for Texture Recognition. IEEE International Conference on Computer Vision. Nice, France. 2003, 1: 649-655P
[73] Krystian Milolajczyk, Cordelia Schmid. An affine invariant interest point detector. In Proceedings of the European Conference on Computer Vision. Copenhagen, Denmark. 2002, 128-142P
[74] K. Mikolajczyk and C. Schmid. Scale and affine invariant interest point detectors. International Journal of Computer Vision. 2004, 60(1): 63-86P
[75] K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, T. Kadir, L. Van Gool. A comparison of affine region detectors. International Journal of Computer Vision. 2005, 65(1-2): 43-72P
[76]唐永鹤,卢焕章,侯文杰.基于DOG特征点的序列图像匹配算法.现代电子技术.2008,4:128-130页
[77] David G. Lowe. Object recognition from local scale-invariant features. In international conference on computer vision. Corfu, Greece. 1999, 1150-1157P
[78]李晓明,郑链,胡占义.基于SIFT特征的遥感影响自动配准.遥感学报.2006,10(6):885-892页
[79]李玲玲,李翠华,曾晓明,李保.基于Harris-affine和SIFT特征匹配的图像自动配准.华中科技大学学报.2008,36(8):13-16页
[80] E.Loupias, N.Sebe, S.Bres, J.Jolion. Wavelet-based Salient Points for Image Retrieval. International Conference on Image Processing. Vancouver, Canada. 2000, 2: 518-521P
[81] Q. Tian and N. Sebe, M. S. Lew, E. Loupias, T. S. Huang. Content-Based Image Retrieval Using Wavelet-based Salient Points. Proceeding of SPIE. 2001, 4315: 425-436P
[82] Andre Hegerath, Thomas Deselaers, Hermann Ney. Patch-based object recognition using discriminatively trained gaussian mixtures. British machine vision conference. Edinburgh, UK, 2006, 2: 519-528P
[83] Andreas Opelt, Axel Pinz, Michael Fussenegger, Peter Auer. Generic object recognition with boosting. IEEE Transactions onpattern analysis and machine intelligence. 2006, 28(3): 416-431P
[84] Bernt Schiele, Hames L. Crowley. Recognition without correspondence using multidimensional receptive field histograms. International Journal of computer vision. 2000, 36(1): 31-52P
[85] Manjunath, B.S., W.Y. Ma. Texture Features for Browsing and Retrieval of Image Data. IEEE PAMI. 1996. 18(8): 837-842P
[86] B. Thai, G. Healey. Modeling and classifying symmetries using a multiscale opponent color representation. IEEE Trans. Pattern Anal. Machine Intell. 1998, 20(11): 1224-1235P
[87] D. Hall, V. deVerdiere, J. Crowley. Object recognition using coloured receptive fields. In Europian Conference on Computer Vision. Springer, Berlin. 2000, 164-177P
[88] Minh A. Hoang, Jan-Mark Geusebroek, Arnold W.M. Smeulders. Color texture measurement and segmentation. Signal processing. 2005, 85: 265-275P
[89] J. Geusebroek, R. van den Boomgaard, A. Smeulders, A. Dev. Color and scale: the spatial structure of color images. In: 6th Europian Conference on Computer Vision. Springer, Berlin. 2000, 331-341P
[90] M. Varma, A. Zisserman. Classifying Images of Materials: Achieving Viewpoint and Illumination Independence. Proc. ECCV. Copenhagen, Denmark. 2002, 3: 255-271P
[91] S. Lazebnik, C. Schmid, J. Ponce. A sparse texture representation using affine invariant regions. In Proc. CVPR. Madison, WI, USA. 2003, 2: 319-324P
[92] Svetlana Lazebnik, Cordelia Schmid, Jean Ponce. A sparse texture representation using local affine regions. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2005, 27: 1265-1278P
[93] David G. Lowe. Distinctive image features from scale-invariant keypoints. International journal of computer vision. 2004, 60(2): 91-110P
[94] Li Fei-fei, Rob Fergus, Pietro Perona. One-shot learning of object categories. IEEE Transactions on pattern analysis and mathine intelligence. 2006, 28(4): 594-611P
[95] Yan Ke, Rahul Sukthankar. PCA-SIFT: A More Distinctive Representation for Local Image Descriptors. IEEE Conference onComputer Vision and Pattern Recognition. Washington DC, USA. 2004, 2: 506-513P
[96] Krystian Mikolajczyk, Cordelia Schmid. A performance evaluation of local descriptors. IEEE Transactions on Pattern analysis and machine intelligence. 2005, 27(10): 1615-1630P
[97] Herbert Bay, Andreas Ess, Tinne Tuytelaars, Luc Van Gool. Speeded-up robust features(SURF). Computer vision and image understanding. 2008, 110(3): 346-359P
[98] A. Bosch, A. Zisserman, X. Munoz. Representing shape with a spatial pyramid kernel. In Proceedings of the ACM International Conference on Image and Video Retrieval. Amsterdam, The Netherlands. 2007, 401-408P
[99] J. van de Weijer, T. Gevers, A. Bagdanov. Boosting color saliency in image feature detection. IEEE Trans. Pattern Analysis and Machine Intell., 2006, 28(1): 150-156P
[100] Alaa E. Abdel-Hakim, Aly A. Farag. CSIFT: a SIFT descriptor with color invariant characteristics. Proc. Conference on Computer Vision and Pattern Recognition. New York, USA. 2006, 2: 1978-1983P
[101] J. M. Geusebroek, R. van den Boomgaard, A. W. M. Smeulders, H. Geerts. Color invariance. IEEE Trans. Pattern Analysis Machine Intelligence. 2001, 23(12): 1338-1350P
[102] Gertjan J. Burghouts, Jan-Mark. Geusebroek. Performance evaluation of local colour invariants. Computer vision and image understanding. 2009, 113(1): 48-62P
[103] T. Ojala, M. Pietikainen, D. Harwood. A comparative study of texture measures with classification based on feature distributions. Pattern Recognition. 1996, 29(1): 51-59P
[104] M. Pietik?inen, T. Ojala, Z. Xu. Rotation-invariant texture classification using feature distributions. Pattern Recognition. 2000, 33: 43-52P
[105] Timo Ojala, Matti Pietik?inen, Topi M?enp??. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactins on pattern analysis and machine intelligence. 2002, 24(7): 971-987P
[106] M. Heikkila, M. Pietikainen, C. Schmid. Description of interest regions with center-symmetric local binary patterns. In: Indian Conference on Computer Vision. Graphics and Image Processing.Madurai, India. 2006, 4338: 58-69P
[107] M. Heikkila, M. Pietikainen, C. Schmid. Descriptor of interest regions with local binary patterns. Pattern recognition. 2009, 42(3): 425-436P
[108] Peng Chang, J. Krumm. Object recognition with color cooccurrence histograms. In Proc. Computer Vision and Pattern Recognition. Fort Collins, Colorado. 1999, 2: 498-504P
[109] M. K. Hu. Visual pattern recognition by moment invariants. IRE Trans. Information Theory. 1962, 8: 179-187P
[110] M.R. Teague. Image analysis via the general theory of moments. J. Opt. Soc. Am. 1980, 70: 920-930P
[111] J. Flusser, T. Suk. Pattern recognition by a$ne moment invariants. Pattern Recognition. 1993, 26: 167-174P
[112] T.M. Hupkens, J. de Clippeleir. Noise and intensity invariant moments. Pattern Recognition. 1995, 16: 371-376P
[113] J. Flusser, T. Suk. Affine moment invariants: A new tool for character recognition. Pattern Recognition Letters. 1994, 15: 433-436P
[114] L. Wang, G. Healey. Using Zernike moments for the illumination and geometry invariant classification of multispectral texture. IEEE Trans. Image Process. 1998, 7: 196-203P
[115] T.H. Reiss. The revised fundamental theorem of moment invariants. IEEE Trans. Pattern Anal. Mach. Intell.. 1991, 13: 830-834P.
[116] Jan Flusser. On the independence of rotation moment invariants. Pattern recognition. 2000, 33: 1405-1410P
[117] Florica Mindru, Tinne Tuytelaars, Luc Van Gool, Theo Moons. Moment invariants for recognition under changing viewpoint and illumination. Computer vision and image understanding. 2004, 3(27): 3-27P
[118] F. Mindru, T. Moons, L. Van Gool. Color-based moment invariants for the viewpoint and illumination independent recognition of planar color patterns. In: Proc. ICAPR. 1998, 113-122P
[119] Cosmin Ancuti, Philippe Bekaert. SIFT-CCH: Increasing the SIFT distinctness by Color Co-occurrence Histograms. International Symposium on Image and Signal Processing and Analysis. 2007, 130-135P
[120] Eric N. Mortensen, Hongli Deng, Linda Shapiro. A SIFT descriptor with gloval context. Computer Vision and Pattern Recognition.1: 778-782P
[121] Canlin Li, Lizhuang Ma. A new framework for feature descriptor based on SIFT. Pattern recognition letters. 2009, 30: 544-557P.
[122] T. Gevers, Arnold W.M. Smeulders. Color based object recognition. Pattern recognition. 1997, 32: 458-464P
[123] Nowak Eric, Jurie Frédéric, Triggs Bill. Sampling Strategies for Bag-of-Features Image Classification. In Proceedings of the European Conference on Computer Vision. Beijing, China. 2006, 3954: 490-503P
[124] Bastian Leibe, Bernt Schiele. Interleaved object categorization and segmentation. In British machine vision conference. Norwich, UK. 2003, 759-768P
[125] A. Meyerson, L. O’Callaghan, S. Plotkin. A k-median algorithm with running time independent of data size. Machine Learning. 2004, 56(1–3): 61-87P. 2004
[126] Frederic Jurie, Bill Triggs. Creating Efficient Codebooks for Visual Recognition. International conference on computer vision. Beijing, China. 2005, 1: 604-610P
[127] A. Estabrooks, T. Jo, and N. Japkowicz. A multiple resampling method for learning from imbalanced data sets. Computational Intelligence, 2004, 20(1): 18-36P
[128] A. Meyerson. Online facility location. In 42nd IEEE Symp. Foundations of Computer Science. 2001, 426-433P
[129]李滔,王俊普,吴秀清,张邵一.基于改进的LBG算法的SVM学习策略.复旦学报.2004,43(5):789-792页
[130] Linde Y, Buzo A, Gray R. An algorithm for vector quantizer design. IEEE Trans on communication, 1980, 28(1): 84-95P
[131] J. Lampinen, E. Oja. Clustering properties of hierarchical self-organizing maps.Journal of mathematical imaging and vision. 1992, 2(2-3): 261-272P
[132]张毓敏,谢康林.基于SOM算法实现的文本聚类.计算机工程.2004,30(1):75-77页
[133] Alahakoon D., Srinivasan B., Halgamuge SK. Dynamic self-organizing maps with controlled growth for knowledge discovery. IEEE Transactions on Neural Networks. 2000, 11(3): 601-614P
[134]王维彬,钟润添.一种基于贪心EM算法学习GMM的聚类算法.计算机仿真.2007,24(2):65-68页
[135]周欢,黄立平.基于SOM神经网络的C-均值聚类算法.计算机应用.2007,27:51-52页
[136] T.M. Cover, P.E. Hart. Nearest Neighbor Pattern Classification. IEEE Trans. Information Theory. 1968, 13: 21-27P
[137] R. Paredes, J Perez-Cortes, A.Juan, E.Vidal.. Local representations and a direct voting scheme for face recognition. Workshop pattern recognition in information system. 2001, 71-79P
[138] Stefan Berchtold, Bernhard Ertl, Daniel A. Keim, Hans-Peter Kriegel, Thomas Seidl. Fast Nearest Neighbor Search in High-dimensional Space. In Proceedings of International Conference on Data Engineering. Orlando, Florida, USA. 1998, 209-218P
[139] Bin Zhang, Sargur N. Srihari. Fast k-nearest neighbor classification using cluster-based trees. IEEE Transactions on pattern analysis and machine intelligence. 2004, 26(4): 525-528P
[140] Ye Nong,Li Xiangyang.A machine learning algorithm based on supervised clustering and classification.In proceeding of conference on active media technology. Hong Kong, China. 2001, 327-334P.
[141] Guo Gongde, Wang Hui, Bell D A,et a1. A kNN Model-Based Approach and its Application in Text Categorization. In:ClCLing, 2004, 559-570P
[142]董立岩,苑森淼,刘光远,贾书洪.基于贝叶斯分类器的图像分类.吉林大学学报.2007,45(2):249-253页
[143]岳晋,杨汝良,宦若虹.贝叶斯理论在多波段SAR图像分类融合中的应用.中国科学院研究生院学报.2008,25(2):257-263页
[144]赵英,刘佳佳.基于贝叶斯定理的遥感图像检索.现代图书情报技术.2006,5:36-39页
[145] Amit David, Boaz Lerner. Support vector machine-based image classification for genetic syndrome diagnosis. Pattern recognition letters. 2005, 26: 1029-1038P
[146]何灵敏,申掌泉,孔繁胜,刘震科.SVM在多源遥感图像分类中的应用研究.中国图像图形学报.2007,12(4):648-654页
[147]蒋芸,李战怀.基于改进的SVM分类器的医学图像分类新方法.计算机应用研究.2008,25(1):53-55页
[148] Pawlak Z. W.. Rough sets and intelligent data analysis.Information sciences. 2002, 147(1-4): 1-12P
[149] S. Wu, S. Amari. Conformal transformation of kernel function: a data-dependent way to improve support vector machine classifiers. Neural processing letters. 2002, 15: 59-67P
[150]安欣,王韬,张录达.一种基于SVM分类的多类识别方法的应用.北京农业学院学报.2006,21(2):20-22页
[151] Melgani F., Bruzzone L.. Classification of hyperspectral remote sensing images with Support Vector Machines. IEEE Transactions on Geoscience and Remote Sensing. 2004, 42: 1778-1790P
[152] Gidudu Anthony, Hulley Gregg, Marwala Tshilidzi. Image classification using SVMs: one-against-one vs one-against-all. Proccedings of Asian conference on remote sensing. Article No. 0711.2914, 2007
[153]杨增照,扬扬,何秀玲,喻莹,董才林.基于和聚类的SVM多类分类方法.计算机应用.2007,27(1):47-49页
[154] G. Csurka, C. Dance, L. Fan, J. Williamowski, C. Bray. Visual categorization with bags of keypoints. In ECCV workshop on Statistical Learning in Computer Vision. 2004, 59-74P
[155] Fergus R., Perona P., Zisserman A.. Object class recognition by unsupervised scale-invariant learning. In: Proc. IEEE Conf. Comp. Vision Patt. Recog. 2003, 2: 264-271P
[156] Josef Sivic, B.C. Russell, A.A. Efros, A. Zisserman, W.T. Freeman. Discovering objects and their localization in images. In: Proc. International conference on computer vision. Beijing, China. 2005, 1: 370-377P
[157] T. Hofmann. Unsupervised learning by probabilistic latent semantic analysis. Machine learning. 2001, 43: 177-196P
[158] L. Fei-Fei, P. Perona. A bayesian hierarchical model for learning natural scene categories. In IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC, USA. 2005, 2: 524-531P
[159] A.Bosch, A.Zisserman, X.Munoz. Scene classification via pLSA. In Proceedings of the European Conference on Computer Vision. Beijing, China. 2006, 517-530P
[160] Eva H?rster,Rainer Lienhart, Malcolm Salaney. Continuous Visual Vocabulary Models for pLSA-Based Scene Recognition. Proceedings of conference on content-based image and video retrieval. Singapore. 2008, 319-328P
[161]王曙燕,耿国华,李丙春.决策树算法在医学图像数据挖掘中的应用.西北大学学报.2005,35(3):262-265页
[162]徐卫东,尹球,匡定波.小波变换在高光谱决策树分类中的应用研究.遥感学报.2006,10(2):204-210页
[163]程鹏,宋余庆,朱玉全,吴微.基于粗糙集和决策树的医学图像分类研究.计算机工程与应用.2008,44(6):243-245页
[164] Jiwoon Jeon, R. Manmatha. Using maximum entropy for automatic image annotation. In proc.of Conference on Image and Video Retrieval. Dublin, Ireland. 2004, 24-32P
[165] Deselaers T., A. Hegerath, D. Keysers, H. Ney. Sparse Patch-Histograms for Object Classification in Cluttered Images. DAGM. Berlin, Germany. 2006, 4174: 202-211P
[166] Ilkay Ulusoy, Christopher M. Bishop. Generative Versus Discriminative Methods for Object Recognition. IEEE conference on computer vision and pattern recognition. San Diego, USA. 2005, 2: 20-25P
[167] K. Barnard, B. Funt. Camera characterization for color research. Color Research and Application. 2002, 27(3): 152-163P
[168]张懿,刘旭,李海峰.自适应图像直方图均衡算法.浙江大学学报.2007.41(4):630-633页
[169] B. V. Funt, G. D. Finlayson. Color Constant Color Indexing. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1995, 17(5): 522-529P
[170] W.Chen, M.J.Er, S.Wu. Illumination Compensation and Normalization Using Logarithm and Discret Cosine Transform. Proc. of IEEE International Conference on Control, Automation, Robotics and Vision. Kunming, China. 2004, 1: 380-385P
[171] L. T. Maloney, B. A. Wandell. Color constancy: A method for recovering surface spectral reflectance. Journal of the Optical Society of America. 1986, 3: 29-33P
[172] K. Barnard. Modeling Scene Illumination Color for Computer Vision and Image Reproduction: A Survey of Computational Approaches. PhD thesis, Simon Fraser University, 2002
[173] K.Tiplitz Blackwell, G. Buchsbaum. Quantitative studies of color constancy. Journal of the Optical Society of America. 1988, 5(10): 1772-1780P
[174] McCann, J.. Lessons learned from mondrians applied to real images and color gamuts. In Proc. IS&T/SID 7th Color ImagingConference, Scottsdale Arizona. 1999, 1-8P
[175] E.H.Land, J.J.McCann.ightness and Retinex theory. Journal of the Optical Society of America.1971, 61: 1-11P
[176] E.H. Land. The Retinex theory of color vision. Science of America. 1977, 237: 108–128P
[177]李学明.基于Retinex理论的图像增强算法.计算机应用,2005,2: 235-237页
[178] E.H. Land. Recent advances in the Retinex Theory and some implications for cortical computations: color vision and the natural image. Proc. Nat. Acad. Sci. USA. 1983, 80: 5163-5169P
[179] Jobson D.J., Rahman Z., Woodell G.A.. A multi-scale Retinex for bridging the gap between color images and the human observation of scenes. IEEE Trans. on Image Processing. 1997, 6(7): 965-976P
[180] A. Blake. Boundary conditions of lightness computation in Mondrian world. Computer vision graphics and image processing. 1985, 32: 314-327P
[181] Brian Funt, Florian Ciurea, and John McCann. Retinex in Matlab. Proceedings of the IS&T/SID Eighth Color Imaging Conference: Color Science, Systems and Applications. Scottsdale, Arizona USA. 2000, 112-121P
[182]刘家朋,赵宇明,胡福乔.基于单尺度Retinex算法的非线性图像增强算法.上海交通大学学报,2007,41(5):685-688页
[183] Rahman Z U, Jobson D J, Woodell G A. Retinex processing for automatic image enhancement. Journal of Electronic Imaging. 2004, 13(1): 100-110P
[184] J.J. McCann, I. Sobel. Experiments with Retinex. HPL Color Summit. 1998
[185] Ron Kimmel, Michael Elad, Doron Shaked, Irwin Sobel. A variational framework for Retinex. International Journal of Computer Vision. 2003, 52(1): 7-23P
[186] Stevne A.Cove, Another look at Land’s Retinex algorithm. IEEE proceedings of Southeastcon’91. 1991, 1: 351-355P
[187] A. C. Hurlbert. Formal connections between lightness algorithms. Journal of the Optical Society of America. 1986, A(3): 1684-1693P
[188] Rahman Z., Jobson D.J., Woodell G.A.. Multi-scale retinex for color image enhancement. International conference on image processing. Hampton, VA, 1996, 3: 1003-1006P
[189]夏思宇,李久贤,夏良正.基于彩色恒常性的彩色图像增强改进算法.南京航天航空大学学报.2006,38(Suppl.):54-57页
[190]笍义斌,李鹏,孙锦涛.一种图形去薄雾方法.计算机应用.2006, 26(1):154-156页
[191]宋凯,沈红,刘昶.多尺度Retinex灰度图像增强.辽宁大学学报.2008, 35(1):46-48页
[192] Wen Wang, Bo Li, Jin Zhang, Shu Xian, Jing Wang. A fast multi-scale Retinex algorithm for color image enhancement. Proceedings of the International Conference on Wavelet Analysis and Pattern Recognition. Hong Kong. 2008, 30-31P
[193] I. T. Young, L. J. van Vliet. Recursive implementation of the Gaussian filtering. Signal Processing. 1995, 44: 139-151P
[194] L. J. van Vliet, I. T. Young, P. W. Verbeek. Recursive Gaussian derivative filters. In Proceedings of the International Conference on Pattern Recognition. Brisbane, Australia. 1998, 509-514P
[195] A Buades, B Coll, JM Morel. A review of image denoising algorithms with a new one. Multiscale modeling and simulation. 2005, 4(2): 490-530P
[196]杨龙光,周激流,何坤.混合噪声的图像复原算法.四川大学学报.2008, 45(5):1120-1124页
[197]付树军,阮秋琦,王文洽.基于特征驱动的双向耦合扩散方程的图像去噪和边缘锐化.光学精密工程.2006,14(2):315-319页
[198]马学磊.基于噪声点检测的中值滤波图像去噪算法.学位论文,西安电子科技大学.2008
[199]张框框,詹凯.探索数码摄影的奥秘.北京,人民邮电出版社,2008
[200] Himayat N, Kassam S.A.. Approsimate performance analysis of edge preserving filters. IEEE Transactions on signal processing. 1993, 41(9): 2764-2777P
[201]谢凤英.Visual C++数字图像处理.北京,电子工业出版社.2008
[202]郑德忠,周颖慧,荆楠.基于GCV准则的小波图像去噪方法研究.2006, 27(6):2268-2270页
[203]米娜,付炜.基于动态信噪比估计的小波阈值去噪新算法.电子测量技术.2008,31(2):70-72页
[204]马义德,张红娟.PCNN与灰度形态学相结合的图像去噪方法.北京邮电大学学报.2008,31(2):108-111页
[205]王雨田.控制论·信息论·系统科学与哲学.北京,中国人民大学出版社,1986
[206] Crutchfield J P, Packard N H. Symbolic dynamics of noisy chaos. Physica D thesis, 1983(7): 201?223P
[207]姚屏,申群太,王俊年.语音信号的谱熵检测在车辆通信中的应用.中南大学学报:自然科学版,2005,36(5):858?862页
[208] Alexei Yavlinsky, Marcus J. P., Daniel Heesch, Stefan Ruger. A comparative study of evidence combination strategies. IEEE International Conference on Acoustics. Speech and Signal Processing. Imperial Coll. London, UK. 2004, 3: 1040-1043P
[209] Di Sciascio E, Donini F M, Mongiello M. Structured Knowledge Representation for Image Retrieval. Journal of Artificial Intelligence Research. 2002, 16: 209-257P
[210] Portilla, J., E.P. Simoncelli. A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients. Int'l Journal of Computer Vision. 2000, 40(1): 49-71P
[211] BS Manjunath, JR Ohm, W Vasudevan, A Yamada. Color and texture descriptors. IEEE Transactions on Circuits and Systems for Video Technology. 2001, 11(6): 703-715P
[212] Clausi, D.A., H. Deng. Fusion of Gabor Filter and Co-occurrence Probability Features for Texture Recognition. IEEE Transactions on Image Processing. 2005, 14(7): 925-936P
[213] Serge Belongie, Jitendra Malik, Jan Puzicha. Shape matching and object recognition using shape contexts. IEEE transactions on pattern analysis and machine intelligence. 2002, 24(24): 509-522P
[214] Hoiem D., Efros A.A., Hebert M.. Geometric context from a single image. IEEE international conference on computer vision. Beijing, China. 2005, 1: 654-661P
[215] Thomas Deselaers, Daniel Keysers, Hermann Ney. Features for image retrieval: an experimental comparison. Information retrieval. 2007, 11(2): 77-107P
[216] Timor Kadir, Michael Brady. Saliency, Scale and Image Description. International Journal of Computer Vision. 2001, 45(2): 83-105P
[217] Charles Goodwin. Practices of Color Classification. Culture and Activity. 2000, 7(1-2): 19-36P
[218] R. Fergus, P. Perona, A. Zisserman. A sparse object category model for efficient learning and exhaustive recognition. IEEE conference on computer vision and pattern recognition. San Diego,CA, USA. 2005, 1: 380-387P
[219] Dimitri A. Lisin, Marwan A. Mattar, Matthew B. Blaschko, Erik G. Learned-Miller, Mark C. Benfield. Combining Local and Global Image Features for Object Class Recognition. IEEE Conference on Computer Vision and Pattern Recognition. San Diego, CA, USA. 2005, 47-55P
[220] T. Ojala, M. Pietik ainen, T. M aenp a. Multi-resolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE PAMI, 2002, 24(7):971-987P
[221] S. Ravela. On Multi-Scale Differential Features and their Representations for Image Retrieval and Recognition. PhD thesis, University of Massachusetts Amherst. 2002
[222] Hedi Harzallah, Cordelia Schmid, Frederic Jurie, Adrien Gaidon. Classification aided two stage localization. INRIA, LEAR project team, PASCAL VOC2008 challenge workshop
[223] N Dalal, B Triggs. Histograms of oriented gradients for human detection. IEEE conference on computer vision and pattern recognition. San Diego, CA, USA. 2005, 1: 886-893P
[224] K. van de Sande, Gevers.T, Snoek C.. Evaluation of color descriptors for object and scene recognition. IEEE on computer vision and pattern recognition. Anchorage, Alaska, USA. 2008, 1-8P
[225] Lazebnic S., Schmid C., Ponce J.. Beyond bag of features: spatial pyramid matching for recognition natural scene categories. IEEE conference on computer vision and pattern recognition. New York, USA. 2006, 2: 2169-2178P
[226] Oliver van Kaick, Greg Mori. Automatic classification of outdoor images by region matching. The 3rd Canadian conference on computer and robot vision. Quebec, Canada. 2006, 9-16P
[227] J. S. Lee, Y.-N. Sun and C.-H. Chen. Multiscale Corner Detection by Using Wavelet Transform. IEEE Trans. Image Processing. 1995, 4(1): 100-104P
[228] Etienne Loupias, Nicu Sebe. Wavelet-Based salient points applications to imae retrieval using color and texture features. Proceedings of the 4th international conference in vision information systems. Lyon, France. 2000, 223-232P
[229] T. Lindeberg. Principles for Automatic Scale Selection. Handbook on Computer Vision and Applications. Academic Press,Boston, USA. 1999, 2: 239-274P
[230] Deselaers T., Keysers D., Ney H.. Discriminative Training for Object Recognition Using Image Patches. IEEE conference on computer vision and pattern recognition. San Diego, CA, USA. 2005, 2: 157-162P
[231] Andre Hegerath. Patch-based Object Recognition. Diploma thesis, Human Language Technology and Pattern Recognition Group, RWTH Aachen University, Aachen, Germany. 2006
[232]张春森.基于点特征匹配的SUSAN,Harris算子比较.西安科技大学.2007.27(4):608-611页
[233] Witkin, A.P. Scale-space filtering. In International Joint Conference on Artificial Intelligence. Karlsruhe, Germany. 1983, 1019-1022P
[234] Minkolajczyk K.. Detection of local feature invariant to affine transformation. Ph.D thesis, Institute National Polytechnique de Grenoble, France. 2002
[235] Nicu Sebe, Michael S. Lew. Comparing salient point detectors. Pattern Recognition Letters. 2003, 24(1-3): 89-96P
[236] Baumberg, A.. Reliable feature matching across widely separated views. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Hilton Head Island, South Carolina, USA. 2000, 774-781P
[237] William T. Freeman, Edward H. Adelson. The design and use of steerable filters. IEEE Transactions on pattern analysis and machine intelligence. 1991, 13(9): 891-906P
[238] AP Ashbrook, NA Thacker, PI Rockett, CI Brown. Robust recognition of scaled shapes using pair wise geometric histograms. Proc. BMVC. 1995, 503-512P
[239] L.J. Van Gool, T. Moons, D. Ungureanu. Affine/Photometric Invariants for Planar Intensity Patterns. Proceedings of the 4th European Conference on Computer Vision. Cambridge, UK. 1996, 1: 642-651P
[240] L. Florack, B. ter Haar Romeny, J. Koenderink, M. Viergever. General Intensity Transformations and Second Order Invariants. Proc. Scandinavian Conf. Image Analysis. Uppsala, Sweden. 1991, 338-345P
[241] A.K. Jain, F. Farrokhnia. Unsupervised Texture Segmentation Using Gabor Filters. Pattern Recognition. 1991, 24(12):1167-1186P
[242] A. Oliva, A. Fussenegger, P. Auer. Weak hypotheses and boosting for generic object detection and recognition. In Proc. European Conference on Computer Vision. Prague, Czech Republic. 2004, 71-84P
[243] A. Torralba, K.P. Murphy, W.T. Freeman, M.A. Rubin. Context-based vision system for place and object recognition. International conference on computer vision. Nice, France. 2003, 1: 273-280P
[244]张秋余,刘洋.使用基于SVM的局部潜在语义索引进行文本分类.计算机应用.2007,27(6):1382-1384页
[245]杨潇茜,王朔中.应用Gabor纹理特征的水管内壁图像分类.上海大学学报.2008,14(6):551-556页
[246] H. Lodhi, J. Shawe-Taylor, N. Christianini, C. Watkins. Text classification using string kernels. Journal of Machine Learning Research. 2002, 2: 419-444P
[247] Y.G. Jiang, C.W. Ngo, J. Yang. Towards optimal bag-of-features for object categorization and semantic video retrieval. In ACM Conference on image and video retrieval. Amsterdam, Netherlands. 2007, 494-501P
[248] Thomas Leung, Jitendra Malik. Representing and recognition the visual appearance of materials using three-dimensional textons. International journal of cpmputer vision. 2001, 43(1): 29-44P
[249] Shivani Agarwal, Dan Roth. Learning a sparse representation for object detection. Proceedings on European conference on computer vision. Copenhagen, Denmark. 2002, 113-130P
[250] David Nistér and Henrik Stewénius. Scalable Recognition with a Vocabulary Tree. In computer vision and pattern recognition. 2006, 2161-2168P
[251] Ville Viitaniemi, Jorma Laaksonen. Experiment on selection of codebooks for local image features histograms. Proc. on vision information system: web-based visual information search and management. Salerno, Italy. 2008, 126-137P
[252]严华.一种改进的k-means算法.计算机与现代化.2009,1:56-59页
[253] Fei-Fei L., Fergus R., Perona P.. Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. In: Proc. IEEE Conf. Comp. Vision Patt. Recog Workshop on Generative-Model BasedVision. Washington, DC, USA. 2004, 178P
[254] Mark Everingham, Andrew Zisserman etc. The 2005 PASCAL visual object classes challenge. http://www.pascal-network.org/challenges/VOC/
[255] M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, A. Zisserman. The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results. http://www.pascal-network.org/challenges/VOC/voc2007/workshop/index.html
[256] Everingham M., Van~Gool L., Williams C. K. I., Winn J., Zisserman, A.. The PASCAL Visual Object Classes Challenge 2008 (VOC2008) Results. http://www.pascal-network.org/challenges/VOC/voc2008/workshop/index.html
[257] Josef Sivic, Bryan C. Russell, Alexei A. Efros, Andrew Zisserman, William T. Freeman. Discovering Object Categories in Image Collections. In Proc. of International conference on computer vision. Beijing, China. 2005, 370-377P
[258] Bryan C. Russell, Alexei A. Efros, Josef Sivic, William T. Freeman, Andrew Zisserman. Using Multiple Segmentations to Discover Objects and their Extent in Image Collections. Proceedings of Computer Vision and Pattern Recognition. New York, USA. 2006, 2: 1605-1614P
[259] T. Hofmann. Unsupervised learning by probabilistic latent semantic analysis. Machine Learning. 2001, 43: 177-196P
[260] A.P. Dempster, N.M. Laird, D.B. Rubin. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society: Series B. 1977, 39(1): 1-38P
[261] S. Kullback. The Kullback-Leibler distance. The American Statistician. 1987, 41: 340-341P
[262]董立岩,苑森淼,刘光远,贾书洪.基于贝叶斯分类器的图像分类.吉林大学学报.2007,45(2):249-253页
[263]陈允杰,张建伟,韦志辉,夏德深,王平安.基于高斯混合模型的活动轮廓模型脑MRI分割.计算机研究与发展.2007,44(9):1595-1603页
[264]肖政宏,王家廒.基于PCA和GMM的图像分类算法.计算机工程与设计.2006,27(11):1951-1953页
[265] Wang Ke-Jun, Ren Zhen, Xiong Xin-yan. Combination of wavelet and SIFT features for image classification using trained Gaussianmixture model. Journal of communication and computer. 2008, 5(10): 41-46P
[266] J. Dahmen. Invariant Image Object Recognition using Gaussian Mixture Densities. Ph.D. thesis, RWTH Aachen University, Aachen, Germany. 2001
[267] Ralf Schluter, Wolfgang Macherey, Boris Müller, Hermann Ney. Comparison of Discriminative Training Criteria and Optimization Methods for Speech Recognition. In Speech Communication. 2001, 34: 287-310P