返回信息流每个主题相关文献很多,只列最基本(must-to-read)的文章,不便详细展开。
目标检测
Paul Viola and Michael J. Jones. Robust Real-time Object Detection, TR 2001.
Rainer Lienhart and Jochen Maydt. An Extended Set of Haar-like Features for Rapid Object Detection,ICIP 2002 (OpenCV).
Ming-Hsuan Yang et al., Detecting Faces in Images: A Survey, PAMI, 2002.
PAUL VIOLA et al., Detecting Pedestrians Using Patterns of Motion and Appearance, IJCV 2005.
背景建模
Chris Stauffer and W.E.L Grimson. Adaptive background mixture models for real-time tracking, CVPR 1999.
AHMED ELGAMMAL et al., Background and Foreground Modeling Using Nonparametric Kernel Density Estimation for Visual Surveillance, 2002.
Dar-Shyang Lee, Effective Gaussian Mixture Learning for Video Background Subtraction, PAMI, 2005.
目标跟踪
MICHAEL ISARD AND ANDREW BLAKE. CONDENSATION—Conditional Density Propagation for Visual Tracking, IJCV 1998.
Dorin Comaniciu et al., Real-Time Tracking of Non-Rigid Objects using Mean Shift, CVPR 2000.
Dorin Comaniciu and Peter Meer. Mean shift a robust approach toward feature space analysis, 2002 PAMI.
Alper Yilmaz et al., Object tracking A survey, ACM 2006 (good starting point).
集成分类
ROBERT E. SCHAPIRE. The Strength of Weak Learnability, ML 1990.
Yoav Freund and Robert E. Schapire. A decision-theoretic generalization of on-line learning, 1996.
Jerome Friedman et al., Additive Logistic Regression: a Statistical View of Boosting, 1998.
Josef Kittler, On combining classifiers, PAMI, 1998.
数据聚类
A.K. JAIN, Data Clustering: A Review, ACM 1999. (starting point, though old).
Ulrike von Luxburg, A Tutorial on Spectral Clustering, TR 2006.
Ulrike von Luxburg and Olivier Bousquet. Limits of Spectral Clustering, NIPS 2004.
Andrew Ng et al., On Spectral Clustering Analysis and an algorithm, NIPS 2002.
Richard Nock and Frank Nielsen, On weighting clustering, PAMI 2006.
Eric P. Xing et al., Distance metric learning, with application to clustering with side-information, NIPS 2002.
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bebekifis
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谢谢bebekifis提供了这么好的资源!
稍微看了一下Ulrike von Luxburg, A Tutorial on Spectral Clustering,文章站在ML的角度详尽的叙述了谱与图的关系,比之Chung的spectral graph theory更适合于计算机的学生阅读。此外,文章还介绍了当前关于graph spectral的一些较好的算法,graph cut, ratio cut,norm cut,随机游走,不错的tutorial。