A new classification model using privileged information and its application

In human's behavior and cognition, teachers always play an important role. However, in the field of machine learning, the information offered by the teacher is seldom applied. In this paper, inspired by Vapnik et al., we propose a fast learning model using privileged information, which uses two smaller-sized Linear Programming (LP) model to take place of a larger Quadratic Programming (QP) model and applies two nonparallel hyperplanes to construct the final classifier. After that, we introduce the Learning model Using Privileged Information (LUPI) into the Visual Tracking Object (VOT) field, which can accelerate the convergence rate of learning and effectively improve the quality. In detail, we give the clear definition of the privileged information about VOT problem and propose a simple but effective on-line object tracking algorithm using privileged information, and all experimental results show the robustness and effectiveness of the proposed method, at the same time show the privileged information provides a great help for further improving the quality.

[1]  Yong Shi,et al.  Laplacian twin support vector machine for semi-supervised classification , 2012, Neural Networks.

[2]  Yuan-Hai Shao,et al.  Improvements on Twin Support Vector Machines , 2011, IEEE Transactions on Neural Networks.

[3]  Horst Bischof,et al.  Real-Time Tracking via On-line Boosting , 2006, BMVC.

[4]  Zhiquan Qi,et al.  Online multiple instance boosting for object detection , 2011, Neurocomputing.

[5]  Yong Shi,et al.  Efficient railway tracks detection and turnouts recognition method using HOG features , 2012, Neural Computing and Applications.

[6]  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.

[7]  Yong Luo,et al.  Manifold Regularized Multitask Learning for Semi-Supervised Multilabel Image Classification , 2013, IEEE Transactions on Image Processing.

[8]  Giovanni Maria Farinella,et al.  MACHINE LEARNING IN COMPUTER VISION , 2002 .

[9]  Weiwei Zhang,et al.  On-Line Ensemble SVM for Robust Object Tracking , 2007, ACCV.

[10]  Horst Bischof,et al.  Semi-supervised On-Line Boosting for Robust Tracking , 2008, ECCV.

[11]  John Shawe-Taylor,et al.  MahNMF: Manhattan Non-negative Matrix Factorization , 2012, ArXiv.

[12]  Vladimir Vapnik,et al.  A new learning paradigm: Learning using privileged information , 2009, Neural Networks.

[13]  Yong Shi,et al.  Structural twin support vector machine for classification , 2013, Knowl. Based Syst..

[14]  Hanzi Wang,et al.  Graph mode-based contextual kernels for robust SVM tracking , 2011, 2011 International Conference on Computer Vision.

[15]  Gang Hua,et al.  Discriminative Tracking by Metric Learning , 2010, ECCV.

[16]  Yong Shi,et al.  ν-Nonparallel support vector machine for pattern classification , 2014, Neural Computing and Applications.

[17]  Rauf Izmailov,et al.  SMO-Style Algorithms for Learning Using Privileged Information , 2010, DMIN.

[18]  Yong Shi,et al.  Robust twin support vector machine for pattern classification , 2013, Pattern Recognit..

[19]  Yong Shi,et al.  Twin support vector machine with Universum data , 2012, Neural Networks.

[20]  Reshma Khemchandani,et al.  Twin Support Vector Machines for Pattern Classification , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Ming-Hsuan Yang,et al.  Robust Object Tracking with Online Multiple Instance Learning , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Xindong Wu,et al.  NESVM: A Fast Gradient Method for Support Vector Machines , 2010, 2010 IEEE International Conference on Data Mining.

[23]  Shai Avidan,et al.  Support vector tracking , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  Bernhard Schölkopf,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2005, IEEE Transactions on Neural Networks.

[25]  Xuelong Li,et al.  Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  Vladimir Vapnik,et al.  Estimation of Dependences Based on Empirical Data: Empirical Inference Science (Information Science and Statistics) , 2006 .

[27]  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).

[28]  Bo Geng,et al.  Manifold Regularized Multi-task Learning for Semi-supervised Multi-label Image Classification , 2013 .

[29]  V. Vapnik,et al.  On the theory of learning with Privileged Information , 2010, NIPS 2010.