视频监控中运动目标分类方法研究
摘要
智能视频监控作为计算机视觉领域的一个新兴的研究方向,在军事和生活领域拥有广阔的发展前景。智能视频监控的主要目的是对视频图像序列中的运动目标进行检测、跟踪、分类和行为分析。目标分类作为智能视频监控的一个关键步骤,其目的是对提取的运动目标进行语义上的分类,为目标跟踪或行为分析提供信息。
本文主要研究基于静止单摄像机的普通户外场景下的运动目标分类。目标分类的准确性是由采集信息的质量决定的。然而,在户外场景下进行信息采集时,难免会受到光照变化、遮挡、天气等外界因素的干扰。运动目标自身姿势、位置、角度的变化也会给信息的采集带来困难。在这样复杂的外界环境下,若要做到准确的分类,同时满足智能视频监控系统实时性的特点,必须选择最能反映目标本质的特征集合,并且提高分类器的分类速度和性能。
本文正是从这两个方面着手,主要进行了以下几方面的工作:
①在特征选择方面,提出了一种新的构造特征集合的算法。首先,提取运动目标的多个特征,然后利用这种算法对每一种特征的分类性能进行评价,最后选出分类贡献率最高的一组特征构成特征集合。通过这种方法进行特征选择后,在保证分类精度的前提下提高了分类速度。
②对局部二进制模式(LBP)进行改进,并将其用于视频目标分类。近年来,局部特征因其较强的鲁棒性,成为研究热点。局部特征在目标识别和图像匹配方面得到了广泛的应用,却很少被用于视频目标分类,主要是因为其计算量较大,且对摄像头的分辨率要求较高。LBP算子是一种有效的纹理描述子,而且计算比较简单。本文将LBP算子用于视频目标分类,取得了不错的效果。
③将经典的机器学习算法AdaBoost算法引用到视频分类中,并将其用于多类分类。AdaBoost算法在构造分类器的同时还完成了对特征的选择。提取LBP算子进行分类时,特征数目庞大,使用AdaBoost算法在对LBP特征进行选择的同时完成了分类器的构造,大大提高了分类的速度和精度。
As an emerging field of computer vision research direction, intelligent video surveillance is widely used in military and civil application. Intelligent video surveillance aims at detecting, tracking, identifying moving objects and understanding objects behaviors through analysis and processing image sequences. Objects classification is an important aspect of intelligent video surveillance whose research content is to classify moving objects into semantically meaningful categories and provide information for tracking or understanding objects behaviors.
The moving object classification of normal outdoor scenes based on static odd-camera is studied in this paper. The accuracy of objects classification is determined by the quality of the gather information. But when the information is being collected in the outdoor, it will be affected by illumination changes、occlusion、weather and other external factors. The change of the position and angle of the objects also affect information collecting. In order to classify objects into right classes and achieve real-time in such a complex external environment, the features that most able to reflect the nature of the objects should be chose, the speed and the performance of the classifier also should be increased.
Considering these two aspects, the main work of this paper is as follows:
1. A new method for feature selection is proposed. Firstly, many features are extracted from the objects. Then this new method is used to evaluate the performance of each feature, and form a good sub-set of features for classification. After using this method for feature selection, the speed of the classifier is greatly improved under the premise of ensuring the classification accuracy.
2. Local Binary Pattern is improved, and used for objects classification. In recent years, local features are more concerned because of its robust. Local features are widely used in objects recognition and image matching. Because of the large amount of calculation and high demanding of video resolution, it is rarely used for objects classification. LBP is an effective texture descriptor, and its calculation is very simple. LBP is used for objects classification in this paper, and the result is good.
3. Adaboost algorithm, as a classical algorithm in machine learning, is used for objects classification in video surveillance. And it is used for multi-class problem. The Adaboost algorithm not only forms a strong classifier, but also selects features. When LBP is used for classification, there is large number of LBP features. The Adaboost algorithm evaluates the performance of each feature, and selects features to form a strong classifier. The speed and performance of the classification are improved.
本文主要研究基于静止单摄像机的普通户外场景下的运动目标分类。目标分类的准确性是由采集信息的质量决定的。然而,在户外场景下进行信息采集时,难免会受到光照变化、遮挡、天气等外界因素的干扰。运动目标自身姿势、位置、角度的变化也会给信息的采集带来困难。在这样复杂的外界环境下,若要做到准确的分类,同时满足智能视频监控系统实时性的特点,必须选择最能反映目标本质的特征集合,并且提高分类器的分类速度和性能。
本文正是从这两个方面着手,主要进行了以下几方面的工作:
①在特征选择方面,提出了一种新的构造特征集合的算法。首先,提取运动目标的多个特征,然后利用这种算法对每一种特征的分类性能进行评价,最后选出分类贡献率最高的一组特征构成特征集合。通过这种方法进行特征选择后,在保证分类精度的前提下提高了分类速度。
②对局部二进制模式(LBP)进行改进,并将其用于视频目标分类。近年来,局部特征因其较强的鲁棒性,成为研究热点。局部特征在目标识别和图像匹配方面得到了广泛的应用,却很少被用于视频目标分类,主要是因为其计算量较大,且对摄像头的分辨率要求较高。LBP算子是一种有效的纹理描述子,而且计算比较简单。本文将LBP算子用于视频目标分类,取得了不错的效果。
③将经典的机器学习算法AdaBoost算法引用到视频分类中,并将其用于多类分类。AdaBoost算法在构造分类器的同时还完成了对特征的选择。提取LBP算子进行分类时,特征数目庞大,使用AdaBoost算法在对LBP特征进行选择的同时完成了分类器的构造,大大提高了分类的速度和精度。
As an emerging field of computer vision research direction, intelligent video surveillance is widely used in military and civil application. Intelligent video surveillance aims at detecting, tracking, identifying moving objects and understanding objects behaviors through analysis and processing image sequences. Objects classification is an important aspect of intelligent video surveillance whose research content is to classify moving objects into semantically meaningful categories and provide information for tracking or understanding objects behaviors.
The moving object classification of normal outdoor scenes based on static odd-camera is studied in this paper. The accuracy of objects classification is determined by the quality of the gather information. But when the information is being collected in the outdoor, it will be affected by illumination changes、occlusion、weather and other external factors. The change of the position and angle of the objects also affect information collecting. In order to classify objects into right classes and achieve real-time in such a complex external environment, the features that most able to reflect the nature of the objects should be chose, the speed and the performance of the classifier also should be increased.
Considering these two aspects, the main work of this paper is as follows:
1. A new method for feature selection is proposed. Firstly, many features are extracted from the objects. Then this new method is used to evaluate the performance of each feature, and form a good sub-set of features for classification. After using this method for feature selection, the speed of the classifier is greatly improved under the premise of ensuring the classification accuracy.
2. Local Binary Pattern is improved, and used for objects classification. In recent years, local features are more concerned because of its robust. Local features are widely used in objects recognition and image matching. Because of the large amount of calculation and high demanding of video resolution, it is rarely used for objects classification. LBP is an effective texture descriptor, and its calculation is very simple. LBP is used for objects classification in this paper, and the result is good.
3. Adaboost algorithm, as a classical algorithm in machine learning, is used for objects classification in video surveillance. And it is used for multi-class problem. The Adaboost algorithm not only forms a strong classifier, but also selects features. When LBP is used for classification, there is large number of LBP features. The Adaboost algorithm evaluates the performance of each feature, and selects features to form a strong classifier. The speed and performance of the classification are improved.
引文
[1]马颂德,张正友.计算机视觉.北京科学出版社, 1998.
[2]王亮,胡卫明,谭铁牛.人运动的视觉分析综述[J].计算机学报,2002,25(3):226-237.
[3]周永顺.视频监控中运动目标检测与识别方法的实验研究[D],内蒙古工业大学硕士毕业论文,2007.12.
[4] S McKenna,S Jabri,Z Duric,etal.Tracking groups of people[J].Computer Vision and Image Understanding,2000,80:42-56.
[5]刘丽丽基于形状特征的运动目标分类方法研究[D],湖南大学硕士毕业论文,2006.5.
[6]邵文坤,黄爱民,韦庆.目标跟踪方法综述.影像技术,2006,(l):115-120
[7] Laurent L., Baldi P. A Principled Approach to Detecting Surprising Events in Video. IEEE Int’l Conf. on Computer Vision and Pattern Recognition (CVPR), June 2005.
[8] Robert T. Collins, Alan J. Lipton, Takeo Kanade, et al. A system for video surveillance and monitoring: VSAM final report CMU-RI-TR-00-12.Pittsburgh: Robotics Institute, Carnegie Mellon University, 2000.
[9] Remagnino P,Tan T and Baker K. Multi-agent visual surveillance of dynamic scenes. Image and Vision Computing, 1998, 16(8):529-532.
[10] Maggioni C, Kammerer B. Gesture Computer: History, Design, and Applications. In: Computer Vision for Human-Machine Interaction. Cambridge: Cambridge University Press,1998.
[11] Freeman W, Weissman C. Television control by hand gestures. In: Proc International Conference on Automatic Face and Gesture Recognition, Zurich, Switzerland: 1995, 179-183.
[12] Haritaoglu I, Harwood D, Davis LW. real-time surveillance of people and their activities[J]. IEEE Trans Pattern Analysis and Machine Intelligence, 2000, 22(8):809-830.
[13]中科院自动化所.智能视频监控为奥运安全保驾护航[EB/OL].http://www.ia.ac.cn,2008.
[14] H. Masnadi-Shirazi and N. Vasconcelos. Asymmetric boosting.In Proceeding of the 24th International Conference on Machine Learning, pages 609{619, Corvallis, OR, 2007.
[15] L.Wang, M. Sugiyama, C. Yang, Z.-H. Zhou, and J. Feng. On the margin explanation of boosting algorithm. In Proceedings of the 21st Annual Conference on Learning Theory, pages 479{490, Helsinki, Finland, 2008.
[16] Timo Ahonen, Abdenour Hadid, and Matti Pietikainen, Face Recognition with Local Binary Patterns. ECCV 2004, LNCS 3021, pp. 469-481, 2004.
[17]韩合民,郭宝龙,赵连凤.基于边缘颜色分布的图像检索方法[J].计算机应用,2007,27(7):1651-1653.
[18]张建国.不变性纹理分析[D].北京:中科院自动化所,2002:51-72.
[19]王晗,白雪冰,王辉.基于空间灰度共生矩阵木材纹理分类识别[J].森林工程,2007,23(1):32-36.
[20]周昌雄,于盛林.一种图像纹理分割的小波-曲线演化方法[J].信息与控制, 2005,34(2):167-171.
[21]余鹏,封举富.基于多分辨率小波和高斯混合模型的纹理图像分割[J].北京大学学报:自然科学版,2005,41(3):338-343.
[22] JAIN A K, VAILAYA A. Image retrieval using color and shape. Pattern Recognition, 1997, 29(8): 1233-1244.
[23]黄元元,何云峰.利用颜色进行基于内容的图像检索[J].小型微型计算机系统,2007,28(7):1277-1281.
[24] Smith J.R., Chang S.F. Tools and Techniques for Color Image Retrieval[A].Proceedings of SPIE Storage and Retrieval for Image and Video Database[C],1995,2670(5):426-437.
[25] Stricker M., Orengo M., Similarity of color images[J].In:Proceedings of SPIE Storage and Retrieval for Image and Video Database,1995,2420:381-392.
[26] Zachary J.M.An Information Theoretic Approach to Content Based Image Retrieval[J] . Louisiana State University and Agricultural and Mechanical College,PhD.Thesis,2000,45-62.
[27] Rafael C, Richard E.数字图像处理.阮秋琦,阮宇智译.第二版.北京:电子工业出版社,2003,530-548.
[28] Daniel Toth, Til Aach. Detection and recognition of moving objects using statistical motion detection and Fourier descriptors. In: Proceedings of the 12th International Conference on Image Analysis and Processing (ICIAP).Mantua, Italy: 2003, 430-435.
[29] Lipton A, Fujiyoshi H, Patil R. Moving target classification and tracking from real-time video. In: Proc IEEE Workshop on Application of Computer Vision, Princeton, NJ: 1998, 8-14.
[30] Kuno Y, Watanabe T, Shimosakoda Y. Automated detection of human for visual surveillance system. In: Proc IEEE International Conference on Pattern Recognition, Vienna: 1996, 865-869.
[31] Cutler R, Davis L. Robust Real-time periodic motion detection analysis, and application. IEEE Trans Pattern Analysis and Machine Intelligence, 2000, 22(8):781-796.
[32] Lipton A. Local application of optic flow to analyse rigid versus non-rigid motion. Tech. report CMU-RI-TR-99-13, Robotics Institute, Carnegie Mellon University, http://www.eecs.lehigh.edu/FRAME/Lipton/iccvframe.html.1999-9.
[33] Selinger A, Wixson L. Classifying moving objects as rigid or non-rigid without correspondences. In: Proc DAPRA Image Understanding Workshop, Monterey, CA, 1998, 1:341-358.
[34] Y. Bogomolov, G. Dror. Classification of moving targets based onmotion and appearance. In: Machine Vision Conference. Rudzsky British: 2003, 429-438.
[35] Lun Zhang, Stan Z. Li, Xiaotong Yuan and Shiming Xiang. Real-time Object Classification in Video Surveillance Based on Appearance Learning. Computer Vision and Pattern Recognition,2007. CVPR '07. IEEE Conference on17-22 June 2007 Page(s):1– 8
[36] Michael Stark, Bernt Schiele. How Good are Local Features for Classes of Geometric Objects. Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on. 14-21 Oct. 2007 Page(s):1– 8.
[37] Zhaoxiang Zhang, Min Li, Kaiqi Huang and Tieniu Tan. Boosting Local Feature Descriptors for Automatic Objects Classification in Traffic Scene Surveillance. In Proc.of the 2008 IEEE International Conference on Pattern Recognition (ICPR2008). Tampa, Florida, USA. December 2008.
[38]高东发,黎绍发.一种基于视频监控的运动目标快速分类方法[J].计算机应用与软件.2006.
[39] Zhaoxiang Zhang, Yinghao Cai, Kaiqi Huang and Tieniu Tan. Real-Time moving object classification with automatic scene division. IEEE Image Processing, 2007: 149–152.
[40] Masamitsu Tsuchiya, Hironobu Fujiyoshi. Evaluating Feature Importance for Object Classification in Visual Surveillance[C]. Pattern Recognition, 2006. ICPR 2006.18th International Conference on. 2006. Volume 2, Page(s):978– 981.
[41] Rudra N. Hota, Vijendran Venkoparao and Anupama Rajagopal. Shape based Object Classification for Automated Video Surveillance with Feature Selection. IEEE Information Technology, 2007 :97–99.
[42] Z.-H. Zhou. Ensemble learning.In S. Z. Li, editor, Encyclopedia of Biometrics. Springer, Berlin, 2008.
[43] Y. Freund and R. E. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1):119{139, 1997.
[44] Y. Freund and R. E. Schapire. A short introduction to boosting. Journal of Japanese Society for Artificial Intelligence, 14(5):771{780, 1999.
[45] R. E. Schapire. The strength of weak learnability. Machine Learning, 5(2):197{227, 1990.
[46] P. Viola and M. Jones. Robust real-time object detection. International Journal of Computer Vision, 57(2):137{154, 2004.
[47] Z.-H. Zhou and Yang Yu. AdaBoost. In: X. Wu and V. Kumar eds. The Top Ten Algorithms in Data Mining, Boca Raton, FL: Chapman & Hall, 2009.
[48] John-Paul Renno, Dimitrios Makris and Graeme A. Jones. Object Classification in Visual Surveillance Using Adaboost[J]. Computer Vision and Pattern Recognition, 2007. CVPR '07. IEEE Conference on.17-22 June 2007 Page(s):1-8.
[49]于昕,韩崇昭,雷明明.支持向量机在目标分类中的应用[J].电子与控制. 2006.
[50] T. Ojala, M. Pietikainen, and D. Harwood. A comparative study of texture measures with classification based on feature distributions. Pattern Recognition, 1996.
[51] T. Ojala, M. Pietikainen, T. Maenpaa. Multiresolution gray scale and rotation invariant texture classification with local binary patterns [J].IEEE Trans. on Pattern Analysis and Machine Intelligence, 2002,24(7):971-987.
[52] T. Maenpaa, M. Pietikainen. Texture analysis with local binary patterns. Handbook of Pattern Recognition and Computer Vision[M].3rd ed, World Scientific, Singapore, 2001:711-736.
[2]王亮,胡卫明,谭铁牛.人运动的视觉分析综述[J].计算机学报,2002,25(3):226-237.
[3]周永顺.视频监控中运动目标检测与识别方法的实验研究[D],内蒙古工业大学硕士毕业论文,2007.12.
[4] S McKenna,S Jabri,Z Duric,etal.Tracking groups of people[J].Computer Vision and Image Understanding,2000,80:42-56.
[5]刘丽丽基于形状特征的运动目标分类方法研究[D],湖南大学硕士毕业论文,2006.5.
[6]邵文坤,黄爱民,韦庆.目标跟踪方法综述.影像技术,2006,(l):115-120
[7] Laurent L., Baldi P. A Principled Approach to Detecting Surprising Events in Video. IEEE Int’l Conf. on Computer Vision and Pattern Recognition (CVPR), June 2005.
[8] Robert T. Collins, Alan J. Lipton, Takeo Kanade, et al. A system for video surveillance and monitoring: VSAM final report CMU-RI-TR-00-12.Pittsburgh: Robotics Institute, Carnegie Mellon University, 2000.
[9] Remagnino P,Tan T and Baker K. Multi-agent visual surveillance of dynamic scenes. Image and Vision Computing, 1998, 16(8):529-532.
[10] Maggioni C, Kammerer B. Gesture Computer: History, Design, and Applications. In: Computer Vision for Human-Machine Interaction. Cambridge: Cambridge University Press,1998.
[11] Freeman W, Weissman C. Television control by hand gestures. In: Proc International Conference on Automatic Face and Gesture Recognition, Zurich, Switzerland: 1995, 179-183.
[12] Haritaoglu I, Harwood D, Davis LW. real-time surveillance of people and their activities[J]. IEEE Trans Pattern Analysis and Machine Intelligence, 2000, 22(8):809-830.
[13]中科院自动化所.智能视频监控为奥运安全保驾护航[EB/OL].http://www.ia.ac.cn,2008.
[14] H. Masnadi-Shirazi and N. Vasconcelos. Asymmetric boosting.In Proceeding of the 24th International Conference on Machine Learning, pages 609{619, Corvallis, OR, 2007.
[15] L.Wang, M. Sugiyama, C. Yang, Z.-H. Zhou, and J. Feng. On the margin explanation of boosting algorithm. In Proceedings of the 21st Annual Conference on Learning Theory, pages 479{490, Helsinki, Finland, 2008.
[16] Timo Ahonen, Abdenour Hadid, and Matti Pietikainen, Face Recognition with Local Binary Patterns. ECCV 2004, LNCS 3021, pp. 469-481, 2004.
[17]韩合民,郭宝龙,赵连凤.基于边缘颜色分布的图像检索方法[J].计算机应用,2007,27(7):1651-1653.
[18]张建国.不变性纹理分析[D].北京:中科院自动化所,2002:51-72.
[19]王晗,白雪冰,王辉.基于空间灰度共生矩阵木材纹理分类识别[J].森林工程,2007,23(1):32-36.
[20]周昌雄,于盛林.一种图像纹理分割的小波-曲线演化方法[J].信息与控制, 2005,34(2):167-171.
[21]余鹏,封举富.基于多分辨率小波和高斯混合模型的纹理图像分割[J].北京大学学报:自然科学版,2005,41(3):338-343.
[22] JAIN A K, VAILAYA A. Image retrieval using color and shape. Pattern Recognition, 1997, 29(8): 1233-1244.
[23]黄元元,何云峰.利用颜色进行基于内容的图像检索[J].小型微型计算机系统,2007,28(7):1277-1281.
[24] Smith J.R., Chang S.F. Tools and Techniques for Color Image Retrieval[A].Proceedings of SPIE Storage and Retrieval for Image and Video Database[C],1995,2670(5):426-437.
[25] Stricker M., Orengo M., Similarity of color images[J].In:Proceedings of SPIE Storage and Retrieval for Image and Video Database,1995,2420:381-392.
[26] Zachary J.M.An Information Theoretic Approach to Content Based Image Retrieval[J] . Louisiana State University and Agricultural and Mechanical College,PhD.Thesis,2000,45-62.
[27] Rafael C, Richard E.数字图像处理.阮秋琦,阮宇智译.第二版.北京:电子工业出版社,2003,530-548.
[28] Daniel Toth, Til Aach. Detection and recognition of moving objects using statistical motion detection and Fourier descriptors. In: Proceedings of the 12th International Conference on Image Analysis and Processing (ICIAP).Mantua, Italy: 2003, 430-435.
[29] Lipton A, Fujiyoshi H, Patil R. Moving target classification and tracking from real-time video. In: Proc IEEE Workshop on Application of Computer Vision, Princeton, NJ: 1998, 8-14.
[30] Kuno Y, Watanabe T, Shimosakoda Y. Automated detection of human for visual surveillance system. In: Proc IEEE International Conference on Pattern Recognition, Vienna: 1996, 865-869.
[31] Cutler R, Davis L. Robust Real-time periodic motion detection analysis, and application. IEEE Trans Pattern Analysis and Machine Intelligence, 2000, 22(8):781-796.
[32] Lipton A. Local application of optic flow to analyse rigid versus non-rigid motion. Tech. report CMU-RI-TR-99-13, Robotics Institute, Carnegie Mellon University, http://www.eecs.lehigh.edu/FRAME/Lipton/iccvframe.html.1999-9.
[33] Selinger A, Wixson L. Classifying moving objects as rigid or non-rigid without correspondences. In: Proc DAPRA Image Understanding Workshop, Monterey, CA, 1998, 1:341-358.
[34] Y. Bogomolov, G. Dror. Classification of moving targets based onmotion and appearance. In: Machine Vision Conference. Rudzsky British: 2003, 429-438.
[35] Lun Zhang, Stan Z. Li, Xiaotong Yuan and Shiming Xiang. Real-time Object Classification in Video Surveillance Based on Appearance Learning. Computer Vision and Pattern Recognition,2007. CVPR '07. IEEE Conference on17-22 June 2007 Page(s):1– 8
[36] Michael Stark, Bernt Schiele. How Good are Local Features for Classes of Geometric Objects. Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on. 14-21 Oct. 2007 Page(s):1– 8.
[37] Zhaoxiang Zhang, Min Li, Kaiqi Huang and Tieniu Tan. Boosting Local Feature Descriptors for Automatic Objects Classification in Traffic Scene Surveillance. In Proc.of the 2008 IEEE International Conference on Pattern Recognition (ICPR2008). Tampa, Florida, USA. December 2008.
[38]高东发,黎绍发.一种基于视频监控的运动目标快速分类方法[J].计算机应用与软件.2006.
[39] Zhaoxiang Zhang, Yinghao Cai, Kaiqi Huang and Tieniu Tan. Real-Time moving object classification with automatic scene division. IEEE Image Processing, 2007: 149–152.
[40] Masamitsu Tsuchiya, Hironobu Fujiyoshi. Evaluating Feature Importance for Object Classification in Visual Surveillance[C]. Pattern Recognition, 2006. ICPR 2006.18th International Conference on. 2006. Volume 2, Page(s):978– 981.
[41] Rudra N. Hota, Vijendran Venkoparao and Anupama Rajagopal. Shape based Object Classification for Automated Video Surveillance with Feature Selection. IEEE Information Technology, 2007 :97–99.
[42] Z.-H. Zhou. Ensemble learning.In S. Z. Li, editor, Encyclopedia of Biometrics. Springer, Berlin, 2008.
[43] Y. Freund and R. E. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1):119{139, 1997.
[44] Y. Freund and R. E. Schapire. A short introduction to boosting. Journal of Japanese Society for Artificial Intelligence, 14(5):771{780, 1999.
[45] R. E. Schapire. The strength of weak learnability. Machine Learning, 5(2):197{227, 1990.
[46] P. Viola and M. Jones. Robust real-time object detection. International Journal of Computer Vision, 57(2):137{154, 2004.
[47] Z.-H. Zhou and Yang Yu. AdaBoost. In: X. Wu and V. Kumar eds. The Top Ten Algorithms in Data Mining, Boca Raton, FL: Chapman & Hall, 2009.
[48] John-Paul Renno, Dimitrios Makris and Graeme A. Jones. Object Classification in Visual Surveillance Using Adaboost[J]. Computer Vision and Pattern Recognition, 2007. CVPR '07. IEEE Conference on.17-22 June 2007 Page(s):1-8.
[49]于昕,韩崇昭,雷明明.支持向量机在目标分类中的应用[J].电子与控制. 2006.
[50] T. Ojala, M. Pietikainen, and D. Harwood. A comparative study of texture measures with classification based on feature distributions. Pattern Recognition, 1996.
[51] T. Ojala, M. Pietikainen, T. Maenpaa. Multiresolution gray scale and rotation invariant texture classification with local binary patterns [J].IEEE Trans. on Pattern Analysis and Machine Intelligence, 2002,24(7):971-987.
[52] T. Maenpaa, M. Pietikainen. Texture analysis with local binary patterns. Handbook of Pattern Recognition and Computer Vision[M].3rd ed, World Scientific, Singapore, 2001:711-736.