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融合局部结构和差异信息的监督特征提取算法 被引量:23

Supervised Feature Extraction Based on Information Fusion of Local Structure and Diversity Information
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摘要 针对监督局部保持投影(Supervised locality preserving projection,SLPP)存在过学习和不能较好地保持图像空间的差异信息等问题,造成算法性能不够好,提出了一种新的基于流形学习的监督特征提取方法(Supervised local structureand diversity projection,S-LSDP).S-LSDP从信息统计量角度引入差异信息,并给出度量差异信息大小的准则(差异离散度)及明确的物理含义;然后通过最小化局部离散度和最大化差异离散度准则提取投影方向.投影后的特征既能有效地保持图像之间的局部结构属性,又能较好地保持图像之间的差异信息,而且避免了过学习问题.在UMIST,Yale,PIE和AR数据库上的实验结果表明了该算法的有效性. Supervised locality preserving projection (SLPP) seeks to find the projection which efficiently preserves the local structure of data points embedded in high-dimensional data space.However,it has the over-learning problem and does not preserve the diversity information of data which is also useful for data recognition.A novel feature extraction method based on manifold learning,namely supervised local structure and diversity projection (S-LSDP),is presented to address this problem.The S-LSDP introduces the diversity of data points from the perspective of statistic and then calculates diversity scatter via the diversity of data points to measure the diversity information of data.A concise feature extraction criterion is raised by minimizing the local scatter,which efficiently preserves the local structure and simultaneously maximize the diversity scatter.Different from the most existing manifold learn methods,the S-LSDP not only preserves both the local structure and diversity information of data,but also avoids the data over-fitting problem.Extensive experiments in UMIST,Yale,PIE,and AR face database show the efficiency of the proposed method.
出处 《自动化学报》 EI CSCD 北大核心 2010年第8期1107-1114,共8页 Acta Automatica Sinica
基金 国家自然科学基金(60802075 60872141) 综合业务网理论及关键技术国家重点实验室自主研究课题(ISN090403) 高等学校学科创新引智计划(B08038)资助~~
关键词 特征提取 流形学习 局部离散度 差异离散度 人脸识别 Feature extraction manifold learning locality scatter diversity scatter face recognition
作者简介 高全学 西安电子科技大学副教授.2005年获得西北工业大学自动化学院博士学位.主要研究方向为统计模式识别,机器学习,人脸识别.本文通信作者.E—mail:qxgao@xidian.edu.cn 谢德燕 西安电子科技大学硕士研究生.2007年获得鲁东大学学士学位.主要研究方向为通信信号处理,数字信号处理,人脸识别.E-mail:xdy0306@163.com 徐辉 西安电子科技大学硕士研究生.2007年获得合肥学院学士学位.主要研究方向为数字信号处理,人脸识别.E-mail:haxhuiily@yahoo.cn 李远征 西安电子科技大学博士研究生.主要研究方向为视觉目标跟踪,智能视频监控,模式识别.E-mail.1iyuanzheng@tom.com 高西全 西安电子科技大学教授.主要研究方向为通信信号处理,数字信号与数字图像处理.E-mail:xqgao@man.xidian.edu.cn
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参考文献18

  • 1Yan S C, Xu D, Zhang B, Zhang H, Yang Q, Lin S. Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(1): 40-51.
  • 2刘青山,卢汉清,马颂德.综述人脸识别中的子空间方法[J].自动化学报,2003,29(6):900-911. 被引量:117
  • 3Murase H, Nayar S K. Visual learning and recognition of 3-D objects from appearance. International Journal of Computer Vision, 1995, 14(1): 5-24.
  • 4Turk M A, Pentland A P. Face recognition using eigenfaces. In: Proceedings of the Conference on Computer Vision and Pattern Recognition. Hawaii, USA: IEEE, 1991. 586-591.
  • 5Belhumeur P N, Hespanha J P, Kriegman D J. Eigenfaces vs fisherfaces: recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): 711-720.
  • 6Tenenbaum J B, de Silva V, Langford J C. A global geometric framework for nonlinear dimensionality reduction. Science, 2000, 290(5500): 2319-2323.
  • 7Seung H S, Lee D D. The manifold ways of perception. Science, 2000, 290(5500): 2268-2269.
  • 8Roweis S T, Saul L K. Nonlinear dimensionality reduction by locally linear embedding. Science, 2000, 290(5500): 2323-2326.
  • 9Belkin M, Niyogi P. Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation, 2003, 15(6): 1373-1396.
  • 10He X F, Yan S C, Hu Y, Niyogi P, Zhang H. Face recognition using Laplacianfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(3): 328-240.

二级参考文献77

  • 1Hjelmas E, Low B K. Face detection: A survey. Journal of Computer Vision and Image Understanding, 2001, 83(3) : 236-274.
  • 2Yang M H, Ahuja N, Kriegman D. Detecting faces in images: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(1): 34-58.
  • 3Toyama K. Prolegomena for robust face tracking. MSR- Tech-Report-98-65, Microsoft, 1998.
  • 4Samal A, lyengar P. Automatic recognition and analysis of human faces and facial expressions: A survey. Pattern recognition, 1992, 25(1) : 65--77.
  • 5Zhao W, Chellappa R, Rosenfeld A, Phillips P J. Face recognition- A literature survey. CS-Tech Report-4167, University of Maryland, 2000.
  • 6Zhou J, Lu C Y, Zhang C S, Li Y D. A survey of face recognition. Acta Electronica Sinica, 2000, 28(4) : 102--106(in Chinese).
  • 7Chellappa R, Wilson C L, Sirohey S. Human and machine recognition of faces: A survey. Proceedings of the IEEE,1995, 83(5): 705--740.
  • 8Bledsoe W. Man-machine facial recognition. Tech Report PRI-22, Panoramic Research Inc., Palo Alto, CA, 1966.
  • 9Belhumeur P N, Hespanha J P, Kriegman D J. Eigenfaces vs Fisherfaee: Recognition using class special linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7) : 711-720.
  • 10Zhao W, Chellappa R, Krishnaswamy A. Discriminant analysis of principal components for face recognition. In:Proceedings of International Conference on Automatic Face and Gesture Recognition, Japan: Nara, 1998. 336-341.

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