On-line Boosting and Vision
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[1] Major Conferences , 1968 .
[2] Ya Tsypkin,et al. Self-learning--What is it? , 1968 .
[3] J. Andel. Sequential Analysis , 2022, The SAGE Encyclopedia of Research Design.
[4] H. Barlow. Vision: A computational investigation into the human representation and processing of visual information: David Marr. San Francisco: W. H. Freeman, 1982. pp. xvi + 397 , 1983 .
[5] S Ullman,et al. Shifts in selective visual attention: towards the underlying neural circuitry. , 1985, Human neurobiology.
[6] J. Lindy. Books , 1985, The Lancet.
[7] E. Ziegel,et al. Artificial intelligence and statistics , 1986 .
[8] Stephen Grossberg,et al. Competitive Learning: From Interactive Activation to Adaptive Resonance , 1987, Cogn. Sci..
[9] N. Littlestone. Learning Quickly When Irrelevant Attributes Abound: A New Linear-Threshold Algorithm , 1987, 28th Annual Symposium on Foundations of Computer Science (sfcs 1987).
[10] G. Watts,et al. Journals , 1881, The Lancet.
[11] Geoffrey E. Hinton,et al. Adaptive Mixtures of Local Experts , 1991, Neural Computation.
[12] Martin Anthony,et al. Computational learning theory: an introduction , 1992 .
[13] Manfred K. Warmuth,et al. The Weighted Majority Algorithm , 1994, Inf. Comput..
[14] Greg Welch,et al. Welch & Bishop , An Introduction to the Kalman Filter 2 1 The Discrete Kalman Filter In 1960 , 1994 .
[15] Yoav Freund,et al. A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.
[16] Thomas G. Dietterich. What is machine learning? , 2020, Archives of Disease in Childhood.
[17] Yu Hen Hu,et al. On-line learning for active pattern recognition , 1996, IEEE Signal Processing Letters.
[18] Avrim Blum,et al. On-line Algorithms in Machine Learning , 1996, Online Algorithms.
[19] Cordelia Schmid,et al. Local Grayvalue Invariants for Image Retrieval , 1997, IEEE Trans. Pattern Anal. Mach. Intell..
[20] Yoav Freund,et al. Boosting the margin: A new explanation for the effectiveness of voting methods , 1997, ICML.
[21] Pat Langley,et al. Selection of Relevant Features and Examples in Machine Learning , 1997, Artif. Intell..
[22] Federico Girosi,et al. Training support vector machines: an application to face detection , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[23] Nathan Intrator,et al. Complex cells and Object Recognition , 1997 .
[24] Avrim Blum,et al. The Bottleneck , 2021, Monopsony Capitalism.
[25] Takeo Kanade,et al. Neural Network-Based Face Detection , 1998, IEEE Trans. Pattern Anal. Mach. Intell..
[26] Jiri Matas,et al. On Combining Classifiers , 1998, IEEE Trans. Pattern Anal. Mach. Intell..
[27] Azriel Rosenfeld,et al. From Image Analysis to Computer Vision: Motives, Methods, and Milestones. , 1998 .
[28] Yoram Singer,et al. Improved Boosting Algorithms Using Confidence-rated Predictions , 1998, COLT' 98.
[29] Gregory D. Hager,et al. Efficient Region Tracking With Parametric Models of Geometry and Illumination , 1998, IEEE Trans. Pattern Anal. Mach. Intell..
[30] Takeo Kanade,et al. Probabilistic modeling of local appearance and spatial relationships for object recognition , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).
[31] Kentaro Toyama,et al. Wallflower: principles and practice of background maintenance , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.
[32] Huan Liu,et al. Handling concept drifts in incremental learning with support vector machines , 1999, KDD '99.
[33] Yoav Freund,et al. A Short Introduction to Boosting , 1999 .
[34] David Mumford,et al. Statistics of natural images and models , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).
[35] W. Eric L. Grimson,et al. Adaptive background mixture models for real-time tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).
[36] David G. Lowe,et al. Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.
[37] Horst Bischof,et al. Robust Recognition Using Eigenimages , 2000, Comput. Vis. Image Underst..
[38] Thomas G. Dietterich. Ensemble Methods in Machine Learning , 2000, Multiple Classifier Systems.
[39] J. Langford,et al. FeatureBoost: A Meta-Learning Algorithm that Improves Model Robustness , 2000, ICML.
[40] Nikunj C. Oza,et al. Online Ensemble Learning , 2000, AAAI/IAAI.
[41] J. Friedman. Special Invited Paper-Additive logistic regression: A statistical view of boosting , 2000 .
[42] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[43] Christophe G. Giraud-Carrier,et al. A Note on the Utility of Incremental Learning , 2000, AI Commun..
[44] Patrick J. Flynn,et al. The 20th Anniversary of the IEEE Transactions on Pattern Analysis and Machine Intelligence , 2000, IEEE Trans. Pattern Anal. Mach. Intell..
[45] Paul A. Viola,et al. Boosting Image Retrieval , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).
[46] OneClass GunnarRätsch,et al. SVM and Boosting : One Class , 2000 .
[47] Bernhard P. Wrobel,et al. Multiple View Geometry in Computer Vision , 2001 .
[48] Stuart J. Russell,et al. Experimental comparisons of online and batch versions of bagging and boosting , 2001, KDD '01.
[49] Paul A. Viola,et al. Robust Real-time Object Detection , 2001 .
[50] Shigeo Abe DrEng. Pattern Classification , 2001, Springer London.
[51] Paul A. Viola,et al. Fast and Robust Classification using Asymmetric AdaBoost and a Detector Cascade , 2001, NIPS.
[52] Paul A. Viola,et al. Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.
[53] Shai Avidan,et al. Support Vector Tracking , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.
[54] Harry Shum,et al. Statistical Learning of Multi-view Face Detection , 2002, ECCV.
[55] Robert E. Schapire,et al. Incorporating Prior Knowledge into Boosting , 2002, ICML.
[56] D. Skočaj. Weighted Incremental Subspace Learning , 2002 .
[57] Matti Pietikäinen,et al. Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..
[58] James M. Rehg,et al. Learning a Rare Event Detection Cascade by Direct Feature Selection , 2003, NIPS.
[59] David M. J. Tax,et al. Online SVM learning: from classification to data description and back , 2003, 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718).
[60] Paul A. Viola,et al. Unsupervised improvement of visual detectors using cotraining , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.
[61] Robert E. Schapire,et al. The Boosting Approach to Machine Learning An Overview , 2003 .
[62] Isabelle Guyon,et al. An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..
[63] Marko Heikkilä,et al. A Texture-based Method for Detecting Moving Objects , 2004, BMVC.
[64] Gunnar Rätsch,et al. Soft Margins for AdaBoost , 2001, Machine Learning.
[65] Peter Auer,et al. Weak Hypotheses and Boosting for Generic Object Detection and Recognition , 2004, ECCV.
[66] Maria-Florina Balcan,et al. Co-Training and Expansion: Towards Bridging Theory and Practice , 2004, NIPS.
[67] Alexey Tsymbal,et al. The problem of concept drift: definitions and related work , 2004 .
[68] Yair Weiss,et al. Learning object detection from a small number of examples: the importance of good features , 2004, CVPR 2004.
[69] G LoweDavid,et al. Distinctive Image Features from Scale-Invariant Keypoints , 2004 .
[70] Antonio Torralba,et al. Sharing features: efficient boosting procedures for multiclass object detection , 2004, CVPR 2004.
[71] Tomer Hertz,et al. Boosting margin based distance functions for clustering , 2004, ICML.
[72] Wen Gao,et al. Face recognition using Ada-Boosted Gabor features , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..
[73] Bo Wu,et al. Fast rotation invariant multi-view face detection based on real Adaboost , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..
[74] Robert Givan,et al. Online Ensemble Learning: An Empirical Study , 2000, Machine Learning.
[75] Cynthia Rudin,et al. The Dynamics of AdaBoost: Cyclic Behavior and Convergence of Margins , 2004, J. Mach. Learn. Res..
[76] Dan Roth,et al. Learning to detect objects in images via a sparse, part-based representation , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[77] Ludmila I. Kuncheva,et al. Classifier Ensembles for Changing Environments , 2004, Multiple Classifier Systems.
[78] Yongmin Li,et al. On incremental and robust subspace learning , 2004, Pattern Recognit..
[79] Antonio Torralba,et al. Contextual Priming for Object Detection , 2003, International Journal of Computer Vision.
[80] Vinod Nair,et al. An unsupervised, online learning framework for moving object detection , 2004, CVPR 2004.
[81] Leo Breiman,et al. Bagging Predictors , 1996, Machine Learning.
[82] Ayhan Demiriz,et al. Linear Programming Boosting via Column Generation , 2002, Machine Learning.
[83] Nikunj C. Oza,et al. AveBoost2: Boosting for Noisy Data , 2004, Multiple Classifier Systems.
[84] Horst Bischof,et al. Online Auto-Calibration in Man-Made Worlds , 2005, Digital Image Computing: Techniques and Applications (DICTA'05).
[85] Jiri Matas,et al. WaldBoost - learning for time constrained sequential detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[86] Michael R. Lyu,et al. A semi-supervised active learning framework for image retrieval , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[87] Yuanzhong Li,et al. Shape parameter optimization for Adaboosted active shape model , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.
[88] Paul A. Viola,et al. Detecting Pedestrians Using Patterns of Motion and Appearance , 2005, International Journal of Computer Vision.
[89] Andrew Blake,et al. Sparse Bayesian learning for efficient visual tracking , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[90] A. Leonardis,et al. On-line Conservative Learning for Person Detection , 2005, 2005 IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance.
[91] Y. Freund,et al. Active learning for visual object detection , 2005 .
[92] K. Lebart,et al. Observations on Boosting Feature Selection , 2005, Multiple Classifier Systems.
[93] H. Grabner,et al. Improving AdaBoost Detection Rate by Wobble and Mean Shift ∗ , 2005 .
[94] Jonathan Brandt,et al. Robust object detection via soft cascade , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[95] Yanxi Liu,et al. Online selection of discriminative tracking features , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[96] Xiaojin Zhu,et al. Semi-Supervised Learning Literature Survey , 2005 .
[97] Mubarak Shah,et al. Online detection and classification of moving objects using progressively improving detectors , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[98] Horst Bischof,et al. Conservative Visual Learning for Object Detection with Minimal Hand Labeling Effort , 2005, DAGM-Symposium.
[99] Alexei A. Efros,et al. Geometric context from a single image , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.
[100] Stuart J. Russell,et al. Online bagging and boosting , 2005, 2005 IEEE International Conference on Systems, Man and Cybernetics.
[101] Yuanzhong Li,et al. Robust Active Shape Model using AdaBoosted Histogram Classifiers , 2005, MVA.
[102] Bill Triggs,et al. Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[103] Yanxi Liu,et al. Online Selection of Discriminative Tracking Features , 2005, IEEE Trans. Pattern Anal. Mach. Intell..
[104] Stephen Kwek,et al. A boosting approach to remove class label noise , 2005, Fifth International Conference on Hybrid Intelligent Systems (HIS'05).
[105] Fatih Murat Porikli,et al. Integral histogram: a fast way to extract histograms in Cartesian spaces , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[106] Qiang Wu,et al. Learning-Based License Plate Detection Using Global and Local Features , 2006, 18th International Conference on Pattern Recognition (ICPR'06).
[107] Wen Gao,et al. Object detection using spatial histogram features , 2006, Image Vis. Comput..
[108] H. Grabner,et al. Autonomous Learning of a Robust Background Model for Change Detection ∗ , 2006 .
[109] Horst Bischof,et al. Fast Visual Object Identification and Categorization , 2006, NIPS 2006.
[110] Alexei A. Efros,et al. Putting Objects in Perspective , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[111] Horst Bischof,et al. Real-Time Tracking via On-line Boosting , 2006, BMVC.
[112] Tuan Van Pham,et al. Audio-Visual Feature Extraction for Semi-Automatic Annotation of Meetings , 2006, 2006 IEEE Workshop on Multimedia Signal Processing.
[113] William W. Cohen,et al. Single-pass online learning: performance, voting schemes and online feature selection , 2006, KDD '06.
[114] Pietro Perona,et al. One-shot learning of object categories , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[115] Mubarak Shah,et al. A Multiview Approach to Tracking People in Crowded Scenes Using a Planar Homography Constraint , 2006, ECCV.
[116] Martin J. Johnson,et al. Real-time Computation of Haar-like features at generic angles for detection algorithms , 2006 .
[117] Horst Bischof,et al. Fast Approximated SIFT , 2006, ACCV.
[118] Horst Bischof,et al. Real-Time Tracking with On-line Feature Selection , 2006 .
[119] Ramakant Nevatia,et al. Improving Part based Object Detection by Unsupervised, Online Boosting , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.
[120] Horst Bischof,et al. Learning Features for Tracking , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.
[121] Ting Yu,et al. Gradient Feature Selection for Online Boosting , 2007, 2007 IEEE 11th International Conference on Computer Vision.
[122] Bernt Schiele,et al. Robust Object Detection with Interleaved Categorization and Segmentation , 2008, International Journal of Computer Vision.
[123] Horst Bischof,et al. Recognizing cars in aerial imagery to improve orthophotos , 2007, GIS.
[124] Horst Bischof,et al. Eigenboosting: Combining Discriminative and Generative Information , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.
[125] Takayoshi Yamashita,et al. Online Real Boosting for Object Tracking Under Severe Appearance Changes and Occlusion , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.
[126] Yuan Li,et al. High-Performance Rotation Invariant Multiview Face Detection , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[127] Shai Avidan,et al. Ensemble Tracking , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[128] Jitendra Malik,et al. Learning Globally-Consistent Local Distance Functions for Shape-Based Image Retrieval and Classification , 2007, 2007 IEEE 11th International Conference on Computer Vision.
[129] Horst Bischof,et al. Flea, Do You Remember Me? , 2007, ACCV.
[130] Horst Bischof,et al. On-line Boosting for Car Detection from Aerial Images , 2007, 2007 IEEE International Conference on Research, Innovation and Vision for the Future.
[131] David D. Cox,et al. Opinion TRENDS in Cognitive Sciences Vol.11 No.8 Untangling invariant object recognition , 2022 .
[132] Björn Stenger,et al. Tracking Using Online Feature Selection and a Local Generative Model , 2007, BMVC.
[133] P. Perona,et al. What do we perceive in a glance of a real-world scene? , 2007, Journal of vision.
[134] Fei-Fei Li,et al. OPTIMOL: Automatic Online Picture Collection via Incremental Model Learning , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.
[135] Horst Bischof,et al. A 3D Teacher for Car Detection in Aerial Images , 2007, 2007 IEEE 11th International Conference on Computer Vision.
[136] Horst Bischof,et al. On-line boosting-based car detection from aerial images , 2008 .
[137] Pascal Fua,et al. Multicamera People Tracking with a Probabilistic Occupancy Map , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[138] Dieter Schmalstieg,et al. 3D tracking in unknown environments using on-line keypoint learning for mobile augmented reality , 2008, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.
[139] Ahmed M. Elgammal,et al. Boosting adaptive linear weak classifiers for online learning and tracking , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[140] H. Bischof,et al. An Improved Car Detection using Street Layer Extraction , 2008 .
[141] Horst Bischof,et al. Semi-supervised On-Line Boosting for Robust Tracking , 2008, ECCV.
[142] Jiri Matas,et al. Training sequential on-line boosting classifier for visual tracking , 2008, 2008 19th International Conference on Pattern Recognition.
[143] Nicolas Pinto,et al. Why is Real-World Visual Object Recognition Hard? , 2008, PLoS Comput. Biol..
[144] Bernhard Rinner,et al. Visual on-line learning in distributed camera networks , 2008, 2008 Second ACM/IEEE International Conference on Distributed Smart Cameras.
[145] Luc Van Gool,et al. Action snippets: How many frames does human action recognition require? , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[146] Horst Bischof,et al. Semi-supervised boosting using visual similarity learning , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[147] Horst Bischof,et al. Time Dependent On-line Boosting for Robust Background Modeling , 2008, VISAPP.