SKK-均值算法及其在人脸检测中的应用
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摘要
数据分析是模式识别、机器学习等领域的理论基础。聚类是一种重要的数据分析方法,它是将物理或抽象数据的集合分组成为由彼此相似的对象组成的多个簇(Cluster)的过程。为了使聚类的结果更准确,需要利用数据本身的属性信息,并且采用一些方法来降低错误率。近几年逐渐兴起的半监督学习和核方法能有效解决这两个问题。
人脸检测(Face Detection)是模式识别领域的一个典型应用问题,是指给定任意一幅图像,判断在这幅图像中是否有人脸出现,如果存在则在输入图像中确定所有人脸的位置、大小和姿态的过程。基于肤色特征和聚类方法的人脸检测研究目前引起学者的广泛关注。
本文将半监督学习策略应用到聚类算法中来指导聚类过程,提出了一种半监督核聚类算法——SKK-均值算法。算法利用一定数量的标记样本构成Seed集,作为监督信息来初始化K-均值算法的聚类中心,引导聚类过程并约束数据划分;同时还采用了核方法把输入数据映射到高维特征空间,并用核函数来实现样本之间的距离计算。在UCI数据集上进行了数值实验,并与K-均值算法和核-K-均值算法进行了比较。
基于SKK-均值算法,本文提出了一种肤色模型(Skin Color Model)算法。算法首先对输入图片进行预处理,然后将少量像素点进行标记作为Seed集,再使用SKK-均值算法对肤色像素进行聚类生成肤色簇。依照各个肤色簇的像素点的概率分布,可以得到肤色模型;同时,对肤色簇进行形态学处理,可以得到肤色区域,这些肤色区域可以作为候选人脸(Face candidate)输入到AdaBoost算法中,进行实际的人脸检测。实验表明,使用本文的肤色模型算法可以快速而又准确地得到肤色簇,进而得到肤色模型和候选人脸区域,从而实现人脸检测。
Data Analysis is a significant problem in the Pattern Recognition and Machine Learning fields. A important meyhod of Data Analysis is the Clustering. It is the organization of a collection of patterns into clusters based on similarity. In order to reduce the error rate, the Semi-Supervised Learning (SSL) strategy and Kernel Method have been introduced into the clustering process.
Face Detection (FD) is the process which searches faces in the input image, and gives its position, size and pose if there’s any. It is a typical problem in PR fields. Nowadays, The study of FD algorithm based on skin color feature and clustering method attract more and more attention of researchers.
This dissertation explore a semi-supervised clustering algorithm called Seed Kernel K-means (SKK-means) which is inspired by the kernel method and Seeding strategy based on the classical K-means algorithm. The algorithm uses a certain ratio of data points as the Seeds to generate initial cluster centers, and maps the data into feature space using kernel method. Our algorithm, which can be easily implemented, compares with respect to the other algorithm such as K-means and Kernel K-means, on 3 UCI databases (IRIS, Crabs and New-Thyroid) in some numeric experiment.
This dissertation also raise a Skin Color Model (SCM) algorithm of FD based on SKK-means algorithm. The algorithm pretreated the input image firstly, and labels some pixels as the Seed points, then clustered each pixels of skin color as the Skin Color Clusters (SCC) using SKK-means algorithm. According to the probability of the pixels in SCC, we can obtained the SCM, and we can also obtain the skin regions after morphologic processing of the SCC. These skin regions can be used as the face candidate to put into the AdaBoost algorithm to detect human faces in practicality circumstance. Our experiments shows that it is more rapidly and accurately of our SCM algorithm to obtain the SCC. Thus, the Face Detection can be finally implemented after we got the SCM and face candidate.
人脸检测(Face Detection)是模式识别领域的一个典型应用问题,是指给定任意一幅图像,判断在这幅图像中是否有人脸出现,如果存在则在输入图像中确定所有人脸的位置、大小和姿态的过程。基于肤色特征和聚类方法的人脸检测研究目前引起学者的广泛关注。
本文将半监督学习策略应用到聚类算法中来指导聚类过程,提出了一种半监督核聚类算法——SKK-均值算法。算法利用一定数量的标记样本构成Seed集,作为监督信息来初始化K-均值算法的聚类中心,引导聚类过程并约束数据划分;同时还采用了核方法把输入数据映射到高维特征空间,并用核函数来实现样本之间的距离计算。在UCI数据集上进行了数值实验,并与K-均值算法和核-K-均值算法进行了比较。
基于SKK-均值算法,本文提出了一种肤色模型(Skin Color Model)算法。算法首先对输入图片进行预处理,然后将少量像素点进行标记作为Seed集,再使用SKK-均值算法对肤色像素进行聚类生成肤色簇。依照各个肤色簇的像素点的概率分布,可以得到肤色模型;同时,对肤色簇进行形态学处理,可以得到肤色区域,这些肤色区域可以作为候选人脸(Face candidate)输入到AdaBoost算法中,进行实际的人脸检测。实验表明,使用本文的肤色模型算法可以快速而又准确地得到肤色簇,进而得到肤色模型和候选人脸区域,从而实现人脸检测。
Data Analysis is a significant problem in the Pattern Recognition and Machine Learning fields. A important meyhod of Data Analysis is the Clustering. It is the organization of a collection of patterns into clusters based on similarity. In order to reduce the error rate, the Semi-Supervised Learning (SSL) strategy and Kernel Method have been introduced into the clustering process.
Face Detection (FD) is the process which searches faces in the input image, and gives its position, size and pose if there’s any. It is a typical problem in PR fields. Nowadays, The study of FD algorithm based on skin color feature and clustering method attract more and more attention of researchers.
This dissertation explore a semi-supervised clustering algorithm called Seed Kernel K-means (SKK-means) which is inspired by the kernel method and Seeding strategy based on the classical K-means algorithm. The algorithm uses a certain ratio of data points as the Seeds to generate initial cluster centers, and maps the data into feature space using kernel method. Our algorithm, which can be easily implemented, compares with respect to the other algorithm such as K-means and Kernel K-means, on 3 UCI databases (IRIS, Crabs and New-Thyroid) in some numeric experiment.
This dissertation also raise a Skin Color Model (SCM) algorithm of FD based on SKK-means algorithm. The algorithm pretreated the input image firstly, and labels some pixels as the Seed points, then clustered each pixels of skin color as the Skin Color Clusters (SCC) using SKK-means algorithm. According to the probability of the pixels in SCC, we can obtained the SCM, and we can also obtain the skin regions after morphologic processing of the SCC. These skin regions can be used as the face candidate to put into the AdaBoost algorithm to detect human faces in practicality circumstance. Our experiments shows that it is more rapidly and accurately of our SCM algorithm to obtain the SCC. Thus, the Face Detection can be finally implemented after we got the SCM and face candidate.
引文
[1] Richard O. Duda, Peter E. Hart, David G. Stork著.李宏东,姚天翔等译.模式分类.第2版.机械工业出版社.中信出版社.
[2] Jiawei Han, Micheline Kamber著.范明,孟小峰等译.数据挖掘概念与技术.机械工业出版社. pp185-187
[3]边肇祺,张学工等著.模式识别.清华大学出版社. pp.244-248
[4] Anil K. Jain, Robert P. W. Duin, and Jianchang Mao. Statistical Pattern Recognition: A Review. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, No.1, Jan. 2000 pp.2-3
[5]李铭.自动人脸检测与识别系统中若干问题的研究.北京交通大学博士学位论文
[6] Ming-Hsuan Yang, David J. Kriegman, and Narendra Ahuja. Detecting Faces in Images: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, No.1, Jan. 2002 pp.34-40
[7] T. Sakai, M. Nagao, S.Fujibayashi. Line Extraction and Pattern Recognition in a photograph. Pattern Recognition. 1969. pp.23
[8] Lu X G, Zhou J, Zhang C S. A novel algorithm for rotated human face detection. Proc IEEE Conference on Computer Vision and Pattern Recognition. Hilton Head Island, South Carolina, USA. 2000. pp.760-764
[9]刘明宝,姚鸿勋,高文.彩色图像的实时人脸跟踪方法.计算机学报. 1998, 21(6). Pp.527- 532
[10] Wang J G, Tan T N. A new face detection method based on shape information. Pattern Recognition Letters. 2000, 21(6-7). pp.463- 471
[11] G. Yang, T. S. Huang. Human Face Detection in Complex Background. Pattern Recognition. 1994, vol. 27, no. 1, pp.53-63
[12] Huiyu Zhoua, Yuan Yuan, Abdul H. Sadka. Application of semantic features in face recognition. Pattern Recognition. 2008, 41. pp.3251-3256
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[18] E. Osuna, R. Freund, and F. Girosi. Training Support Vector Machines: An Application to Face Detection. Proc. IEEE Conf. Computer Vision and Pattern Recognition. 1997. pp.130-136
[19] H. Schneiderman and T. Kanade. Probabilistic Modeling of Local Appearance and Spatial Relationships for Object Recognition. Proc. IEEE Conf. Computer Vision and Pattern Recognition. 1998. pp.45-51
[20] K. K. Sung and T. Poggio. Example-Based Learning for View-Based Human Face Detection. IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 1, Jan. 1998. pp.39-51
[21]邓乃扬,田英杰.数据挖掘中的新方法——支持向量机.科学出版社. pp.98-108
[22]李昆仑,曹铮,曹丽苹,张超,刘明.半监督聚类的若干新进展.模式识别与人工智能. 2009年10月,第22卷第5期. pp.735-742
[23]李昆仑,张伟,代运娜.基于Tri-training的半监督SVM.计算机工程与应用. 2009年第22期. pp.103-106
[24] Kunlun Li, Wei Zhang, Xiaotao Ma, Zheng Cao, Chao Zhang. A Novel Semi-supervised SVM Based on Tri-training. IITA 2008.
[25] X.J. Zhu. Semi-supervised learning literature survey. Technical Report 1530, Department of Computer Sciences. University of Wisconsin at Madison, Madison, WI, December, 2007.
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[31] A. Demiriz, K. P. Bennett, and M. J. Embrechts. Semi-supervised clustering using genetic algorithms. Artificial Neural Networks in Engineering,1999 (ANNIE-99). pp.809–814
[32] K. Wagstaff, C. Cardie, S. Rogers and S. Schroedl. Constrained K-means Clustering with Background Knowledge. Proceedings of 18th International Conference on Machine Learning, 2001. pp.577-584
[33] Sugato Basu, Arindam Banerjee, Raymond Mooney. Semi-supervised Clustering by Seeding. International Conference on Machine Learning, 2002. pp.19-26
[34] J. Ye, Z. Zhao, and H. Liu. Adaptive distance metric learning for clustering. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 2007.
[35] Kleind, Kamvar S.D., Manning C. From instance-level constraints to space-level constraints: Making the most of prior knowledge in data clustering. ICML, 2002. pp.307-314
[36] Xing EP, Ngay, Jordanmi, et al. Distance metric learning with application to clustering with side information. Advances in Neural Information Processing Systems. 2003(15). pp.505-512
[37] Zhong S. Semi-Supervised model-based document clustering: A comparative study. Machine Learning, 2006,65(1). pp.3?29
[38] M. Aizerman, E. Braverman, L. Rozonoer. Theoretical foundations of the potential function method in pattern recognition learning. Automat. Remote Control 25 (1964) pp.821–837
[39] C. Cortes, V. Vapnik. Support vector networks. Mach. Learn. 20 (1995). pp.273–297
[40] M. Filippone, F. Camastra, F. Masulli, S. Rovetta. A survey of kernel and spectral methods for clustering. Pattern Recognition, 2008, 41. pp.176-190
[41] Z.D. Wu, W.X. Xie, J.P. Yu. Fuzzy c-means clustering algorithm based on kernel method. Comput.Intell. Multimedia Appl. 2003.
[42] B. Kulis, S. Basu, I.S. Dhillon, R. Mooney. Semi-supervised graph clustering: a kernel approach. ICML’05. Proceedings of the 22nd International Conference on Machine Learning, ACM Press, New York, NY, USA, 2005. pp.457–464
[43] Mark Girolami. Mercer Kernel-Based Clustering in Feature Space. IEEE Transactions on Neural Networks. 2002, Vol.13, No.3. pp.780-784
[44] A.B. Hur, D. Horn, H.T. Siegelmann, V. Vapnik. Support Vector Clustering. J. Mach. Learn. Res. 2 (2001). pp.125–137
[45] Francesco Camastra, Alessandro Verri. A Novel Kernel Method for Clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2005, Vol.27, No.5. pp.801-805
[46] A. P. Dempster, N. M. Laird, D.B. Rubin. Maximum Likelihood from Incomplete Data via the EM Algorithm. J. Royal Statistical Soc: 1977, Vol.39, No.1. pp.1-38
[47] UCI repository of machine learning databases, http://www.ics.uci.edu/mlearn/MLRepository.html
[48] Juwei Lu, K. N. Plataniotis, A. N. Venetsanopoulos, and Stan Z. Li. Ensemble-Based Discriminant Learning With Boosting for Face Recognition. IEEE Trans. on Neural Networks, vol.17, No.1, Jan. 2006
[49] B. Menser and F. Muller. Face Detection in Color Images Using Principle Components Analysis. Image Processing and its Applications, Conference Publication No. 465. IEEE 1999.
[50] M. B. Stegmann, B. K. Ersb?ll, and R. Larsen. FAME a flexible appearance modelling environment. IEEE Trans. on Medical Imaging, 22(10):1319-1331, 2003
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[2] Jiawei Han, Micheline Kamber著.范明,孟小峰等译.数据挖掘概念与技术.机械工业出版社. pp185-187
[3]边肇祺,张学工等著.模式识别.清华大学出版社. pp.244-248
[4] Anil K. Jain, Robert P. W. Duin, and Jianchang Mao. Statistical Pattern Recognition: A Review. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, No.1, Jan. 2000 pp.2-3
[5]李铭.自动人脸检测与识别系统中若干问题的研究.北京交通大学博士学位论文
[6] Ming-Hsuan Yang, David J. Kriegman, and Narendra Ahuja. Detecting Faces in Images: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, No.1, Jan. 2002 pp.34-40
[7] T. Sakai, M. Nagao, S.Fujibayashi. Line Extraction and Pattern Recognition in a photograph. Pattern Recognition. 1969. pp.23
[8] Lu X G, Zhou J, Zhang C S. A novel algorithm for rotated human face detection. Proc IEEE Conference on Computer Vision and Pattern Recognition. Hilton Head Island, South Carolina, USA. 2000. pp.760-764
[9]刘明宝,姚鸿勋,高文.彩色图像的实时人脸跟踪方法.计算机学报. 1998, 21(6). Pp.527- 532
[10] Wang J G, Tan T N. A new face detection method based on shape information. Pattern Recognition Letters. 2000, 21(6-7). pp.463- 471
[11] G. Yang, T. S. Huang. Human Face Detection in Complex Background. Pattern Recognition. 1994, vol. 27, no. 1, pp.53-63
[12] Huiyu Zhoua, Yuan Yuan, Abdul H. Sadka. Application of semantic features in face recognition. Pattern Recognition. 2008, 41. pp.3251-3256
[13] M. J. Jones, J. M. Rehg. Statistical Color Models with Applicaton to Skin Detection. Cambridge Research Lab. Technical Report Series. CRL 98/11, Dec. 1998
[14] Rein-Lien Hsu, Mohamed Abdel-Mottaleb, Anil K. Jain. Face Detection in color images. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, No.5, May. 2002. pp.697-699
[15] P. Sinha. Object Recognition via Image Invariants: A Case Study. Investigative Ophthalmology and Visual Science. vol. 35, no. 4, 1994. pp.1735-1740
[16] T. F. Cootes, C. J. Taylor, D. H. Cooper, and J. Graham. Active Shape Models-Their Training and Application. Computer Vision and Image Understanding. Vol.61, No.1, Jan,1995. pp.38-59
[17] R. Féraud, O.J. Bernier, J.-E. Villet, and M. Collobert. A Fast and Accuract Face Detector Based on Neural Networks. IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 1, Jan. 2001. pp.42-53
[18] E. Osuna, R. Freund, and F. Girosi. Training Support Vector Machines: An Application to Face Detection. Proc. IEEE Conf. Computer Vision and Pattern Recognition. 1997. pp.130-136
[19] H. Schneiderman and T. Kanade. Probabilistic Modeling of Local Appearance and Spatial Relationships for Object Recognition. Proc. IEEE Conf. Computer Vision and Pattern Recognition. 1998. pp.45-51
[20] K. K. Sung and T. Poggio. Example-Based Learning for View-Based Human Face Detection. IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 1, Jan. 1998. pp.39-51
[21]邓乃扬,田英杰.数据挖掘中的新方法——支持向量机.科学出版社. pp.98-108
[22]李昆仑,曹铮,曹丽苹,张超,刘明.半监督聚类的若干新进展.模式识别与人工智能. 2009年10月,第22卷第5期. pp.735-742
[23]李昆仑,张伟,代运娜.基于Tri-training的半监督SVM.计算机工程与应用. 2009年第22期. pp.103-106
[24] Kunlun Li, Wei Zhang, Xiaotao Ma, Zheng Cao, Chao Zhang. A Novel Semi-supervised SVM Based on Tri-training. IITA 2008.
[25] X.J. Zhu. Semi-supervised learning literature survey. Technical Report 1530, Department of Computer Sciences. University of Wisconsin at Madison, Madison, WI, December, 2007.
[26] R. P. Lippmann. Pattern classification using neural networks. IEEE Communications, 1989, 27(11). pp.47-64
[27] B. Shahshahani, D. Landgrebe. The effect of unlabeled samples in reducing the small sample size problem and mitigating the hughes phenomenon. IEEE Transactions on Geoscience and Remote Sensing, 1994, 32(5). pp.1087-1095
[28] Nizar Grira, Michel Crucianu, Nozha Boujemaa. Unsupervised and Semi-supervised Clustering: a Brief Survey. INRIA Rocquencourt, B.P. 105, 78153 Le Chesnay Cedex, France, August 15, 2005.
[29] Joachims T. Transductive inference for text classification using support vector machines. Ivan B, Saso D, eds. Proc. of the 16th Int’l Conf. on Machine Learning (ICML’99). San Francisco. Morgan Kaufmann Publishers, 1999. pp.200?209
[30] Blum A, Lafferty J, Rwebangira M, Reddy R. Semi-Supervised learning using randomized mincuts. Carla EB, ed. Proc. of the 21st Int’l Conf. on Machine Learning (ICML 2004). Banff: ACM Press, 2004. pp.934?947
[31] A. Demiriz, K. P. Bennett, and M. J. Embrechts. Semi-supervised clustering using genetic algorithms. Artificial Neural Networks in Engineering,1999 (ANNIE-99). pp.809–814
[32] K. Wagstaff, C. Cardie, S. Rogers and S. Schroedl. Constrained K-means Clustering with Background Knowledge. Proceedings of 18th International Conference on Machine Learning, 2001. pp.577-584
[33] Sugato Basu, Arindam Banerjee, Raymond Mooney. Semi-supervised Clustering by Seeding. International Conference on Machine Learning, 2002. pp.19-26
[34] J. Ye, Z. Zhao, and H. Liu. Adaptive distance metric learning for clustering. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 2007.
[35] Kleind, Kamvar S.D., Manning C. From instance-level constraints to space-level constraints: Making the most of prior knowledge in data clustering. ICML, 2002. pp.307-314
[36] Xing EP, Ngay, Jordanmi, et al. Distance metric learning with application to clustering with side information. Advances in Neural Information Processing Systems. 2003(15). pp.505-512
[37] Zhong S. Semi-Supervised model-based document clustering: A comparative study. Machine Learning, 2006,65(1). pp.3?29
[38] M. Aizerman, E. Braverman, L. Rozonoer. Theoretical foundations of the potential function method in pattern recognition learning. Automat. Remote Control 25 (1964) pp.821–837
[39] C. Cortes, V. Vapnik. Support vector networks. Mach. Learn. 20 (1995). pp.273–297
[40] M. Filippone, F. Camastra, F. Masulli, S. Rovetta. A survey of kernel and spectral methods for clustering. Pattern Recognition, 2008, 41. pp.176-190
[41] Z.D. Wu, W.X. Xie, J.P. Yu. Fuzzy c-means clustering algorithm based on kernel method. Comput.Intell. Multimedia Appl. 2003.
[42] B. Kulis, S. Basu, I.S. Dhillon, R. Mooney. Semi-supervised graph clustering: a kernel approach. ICML’05. Proceedings of the 22nd International Conference on Machine Learning, ACM Press, New York, NY, USA, 2005. pp.457–464
[43] Mark Girolami. Mercer Kernel-Based Clustering in Feature Space. IEEE Transactions on Neural Networks. 2002, Vol.13, No.3. pp.780-784
[44] A.B. Hur, D. Horn, H.T. Siegelmann, V. Vapnik. Support Vector Clustering. J. Mach. Learn. Res. 2 (2001). pp.125–137
[45] Francesco Camastra, Alessandro Verri. A Novel Kernel Method for Clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2005, Vol.27, No.5. pp.801-805
[46] A. P. Dempster, N. M. Laird, D.B. Rubin. Maximum Likelihood from Incomplete Data via the EM Algorithm. J. Royal Statistical Soc: 1977, Vol.39, No.1. pp.1-38
[47] UCI repository of machine learning databases, http://www.ics.uci.edu/mlearn/MLRepository.html
[48] Juwei Lu, K. N. Plataniotis, A. N. Venetsanopoulos, and Stan Z. Li. Ensemble-Based Discriminant Learning With Boosting for Face Recognition. IEEE Trans. on Neural Networks, vol.17, No.1, Jan. 2006
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