全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

格拉斯曼流形上的半监督判别分析

DOI: 10.11834/jig.20130808

Keywords: 格拉斯曼流形,图像集合,判别分析,半监督

Full-Text   Cite this paper   Add to My Lib

Abstract:

将图像集合表示为格拉斯曼流形上的点能够获得更好的识别性能。传统格拉斯曼流形上的判别分析方法仅考虑了带标签样本的统计信息,忽略了无标签样本。鉴于此,基于流形正则化思想,提出了一个新的格拉斯曼流形上的半监督判别分析方法(SDAGM),将其应用于图像集合的识别问题。通过构建近邻图刻画格拉斯曼流形上的所有样本局部几何结构,并使其作为正则化项添加到格拉斯曼流形上的判别分析目标函数中,本文方法不但考虑标签信息,而且利用了一致性假设。标准数据集上的实验结果表明了SDAGM的有效性。

References

[1]  Belhumeur P N, Hespanha J, Kriegman D J. Eigenfaces vs. fisherfaces: recognition using class specific linear projection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): 711-720. [DOI:10.1109/34.598228]
[2]  Yan S C, Xu D, Zhang B Y, et al. Graph embedding and extensions: a general framework for dimensionality reduction[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(1): 40-51. [DOI:10.1109/TPAMI. 2007. 12]
[3]  Kim M, Kumar S, Pavlovic V, et al. Face tracking and recognition with visual constraints in real-world videos[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Anchorage, Alaska, USA: IEEE Computer Society, 2008:1-8. [DOI:10.1109/CVPR.2008.4587572]
[4]  Arandjelovic O., Shakhnarovich G, Fisher J. Face recognition with image sets using Manifold Density divergence[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. San Diego, CA, USA: IEEE Computer Society, 2005: 581-588. [DOI: 10.1109/CVPR.2005.151]
[5]  Harandi M T, Sanderson C, Wiliem A, et al. Kernel analysis over Riemannian Manifolds for visual recognition of actions, pedestrians and textures[C]//Proceedings of IEEE Workshop on Applications of Computer Vision. Breckenridge, CO, USA: IEEE Computer Society, 2012, 433-439. [DOI: 10.1109/WACV.2012.6163005]
[6]  Sanin A, Sanderson C, Harandi M, et al. K-tangent spaces on Riemannian manifolds for improved pedestrian detection[C]//Proceedings of IEEE International Conference on Image Processing. Orlando, Florida, USA: IEEE Computer Society, 2012:7-10.
[7]  Fan W, Yeung D Y. Locally linear models on face appearance manifolds with application to dual-subspace based classification[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. New York, USA:IEEE Computer Society, 2006:1384-1390. [DOI:10.1109/CVPR.2006.178]
[8]  Yamaguchi O, Fukui K, Maeda K. Face recognition using temporal image sequence[C]//Proceedings of IEEE International Conference on Automatic Face and Gesture Recognition. Nara, Japan: IEEE Computer Society, 1998: 318-323. [DOI: 10.1109/AFGR.1998.670968]
[9]  Fukui K, Yamaguchi O. Face recognition using multiview point patterns for robot vision[C]//International Symposium on Robotics Research.Siena, Italy: Springer Tracts in Advanced Robotics, 2003:192-201.[DOI: 10.1007/11008941_21]
[10]  Kim T K, Kittler J, Cipolla R. Discriminative learning and recognition of image set classes using canonical correlations[J]. IEEE Transactions Pattern Analysis and Machine Intelligence, 2007, 29(6): 1005-1018. [DOI:10.1109/TPAMI. 2007. 1037]
[11]  Chu W S, Chen J C, Lien J J. Kernel discriminant transformation for image set-based face recognition[J]. Pattern Recognition, 2011, 44(8): 1567-1580. [DOI:10.1016/j. patcog. 2011.02.011]
[12]  Li X, Fukui K, Zheng N N. Image-set based face recognition using boosted global and local principal angles[C]//Proceedings of Asian Conference on Computer Vision. Queenstown. New Zealand:the Asian Federation of Computer Vision Societies,2010:323-332.[DOI:10.1007/978-3-642-12307-8_3]
[13]  Wang R, Shan S, Chen X, et al. Manifold-manifold distance with application to face recognition based on image set[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Anchorage, Alaska, USA:IEEE Computer Society, 2008:1-8. [DOI:10.1109/CVPR.2008.4587719]
[14]  Shirazi S, Harandi M T, Sanderson C, et al. Clustering on Grassmann manifolds via kernel embedding with application to action analysis[C]//Proceedings of IEEE International Conference on Image Processing. Orlando, Florida, USA: IEEE Signal Processing Society,2012.
[15]  Hamm J, Lee D D. Grassmann discriminant analysis: a unifying view on subspace-based learning[C]//Proceedings of International Conference on Machine Learning. Helsinki, Finland: ACM, 2008:376-383. [DOI: 10.1145/1390156.1390204]
[16]  Chen J, Ye J, Li Q. Integrating global and local structures: a least squares framework for dimensionality reduction[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Minneapolis, Minnesota, USA:IEEE Computer Society,2007:1-8. [DOI: 10.1109/CVPR.2007.383040]
[17]  Yin X S,Hu E L. Semi-supervised locality dimensionality reduction[J]. Journal of Image and Graphics,2011,16(9):1615-1624. [尹学松,胡恩良.半监督局部维数约减[J].中国图象图形学报,2011,16(9):1615-1624.][DOI:10.11834/jig.20110901]
[18]  Harandi M, Nili M, Ahmadabadi, et al. Optimal local basis: a reinforcement learning approach for face recognition[J]. International Journal of Computer Vision, 2009,81(2):191-204. [DOI: 10.1007/s11263-008-0161-5]
[19]  Harandi M T, Sanderson C, Shirazi S, et al.Graph embedding discriminant analysis on Grassmannian manifolds for improved image set matching[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Colorado Springs, CO, USA:IEEE Computer Society, 2011:2705-2712.[DOI: 10.1109/CVPR.2011.5995564]
[20]  Edelman A, Arias T A, Smith S T. The geometry of algorithms with orthogonality constraints[J]. SIAM Journal on Matrix Analysis and Applications, 1998, 20(2): 303-353. [DOI: 10.1137/S0895479895290954]
[21]  更多...
[22]  Belhumeur P N, Hespanha J P, Kriegman D J. Eigenfaces vs. fisherfaces: recognition using class specific linear projection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): 711-720. [DOI: 10.1109/34. 598228]
[23]  Mika S, Ratsch G, Weston J, et al. Mullers, fisher discriminant analysis with kernels[C]//Proceedings of IEEE Signal Proce-ssing Society Workshop on Neural Networks for Signal Processing IX. Wisconsin, USA: IEEE Signal Processing Society, 1999. [DOI: 10.1109/NNSP.1999.788121.]

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133