Large scale classifiers for visual classification tasks

[1]  François Poulet,et al.  Parallel incremental SVM for classifying million images with very high-dimensional signatures into thousand classes , 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

[2]  François Poulet,et al.  Large Scale Visual Classification with Many Classes , 2013, MLDM.

[3]  Subhransu Maji,et al.  Efficient Classification for Additive Kernel SVMs , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Jianxin Wu,et al.  Power mean SVM for large scale visual classification , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[5]  Cordelia Schmid,et al.  Good Practice in Large-Scale Learning for Image Classification , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Jia Deng,et al.  Large scale visual recognition , 2012 .

[7]  Chia-Hua Ho,et al.  Recent Advances of Large-Scale Linear Classification , 2012, Proceedings of the IEEE.

[8]  Chih-Jen Lin,et al.  Large Linear Classification When Data Cannot Fit in Memory , 2011, TKDD.

[9]  Ming Yang,et al.  Large-scale image classification: Fast feature extraction and SVM training , 2011, CVPR 2011.

[10]  Florent Perronnin,et al.  High-dimensional signature compression for large-scale image classification , 2011, CVPR 2011.

[11]  James M. Rehg,et al.  Efficient and Effective Visual Codebook Generation Using Additive Kernels , 2011, J. Mach. Learn. Res..

[12]  Jianxin Wu,et al.  A Fast Dual Method for HIK SVM Learning , 2010, ECCV.

[13]  Thomas S. Huang,et al.  Image Classification Using Super-Vector Coding of Local Image Descriptors , 2010, ECCV.

[14]  Fei-Fei Li,et al.  What Does Classifying More Than 10, 000 Image Categories Tell Us? , 2010, ECCV.

[15]  Florent Perronnin,et al.  Large-scale image categorization with explicit data embedding , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[16]  Andrew Zisserman,et al.  Efficient additive kernels via explicit feature maps , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[17]  Christopher K. I. Williams,et al.  International Journal of Computer Vision manuscript No. (will be inserted by the editor) The PASCAL Visual Object Classes (VOC) Challenge , 2022 .

[18]  Antonio Torralba,et al.  Semi-Supervised Learning in Gigantic Image Collections , 2009, NIPS.

[19]  Yihong Gong,et al.  Nonlinear Learning using Local Coordinate Coding , 2009, NIPS.

[20]  Andrew Zisserman,et al.  Multiple kernels for object detection , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[21]  Daniel P. Huttenlocher,et al.  Landmark classification in large-scale image collections , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[22]  Fei-Fei Li,et al.  ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[23]  Shuicheng Yan,et al.  Large scale natural image classification by sparsity exploration , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[24]  Claudio Marrocco,et al.  MCS-based balancing techniques for skewed classes: An empirical comparison , 2008, 2008 19th International Conference on Pattern Recognition.

[25]  Chih-Jen Lin,et al.  A sequential dual method for large scale multi-class linear svms , 2008, KDD.

[26]  Thanh-Nghi Do,et al.  A novel speed-up SVM algorithm for massive classification tasks , 2008, 2008 IEEE International Conference on Research, Innovation and Vision for the Future in Computing and Communication Technologies.

[27]  Chih-Jen Lin,et al.  A dual coordinate descent method for large-scale linear SVM , 2008, ICML '08.

[28]  Subhransu Maji,et al.  Classification using intersection kernel support vector machines is efficient , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[29]  Pietro Perona,et al.  Learning and using taxonomies for fast visual categorization , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[30]  Chih-Jen Lin,et al.  Trust Region Newton Method for Logistic Regression , 2008, J. Mach. Learn. Res..

[31]  Thanh-Nghi Do,et al.  A Comparison of Different Off-Centered Entropies to Deal with Class Imbalance for Decision Trees , 2008, PAKDD.

[32]  Yann Guermeur,et al.  SVM Multiclasses, Théorie et Applications , 2007 .

[33]  Y. Singer,et al.  Pegasos: Primal Estimated sub-GrAdient SOlver for SVM , 2011, ICML.

[34]  G. Griffin,et al.  Caltech-256 Object Category Dataset , 2007 .

[35]  Thorsten Joachims,et al.  Training linear SVMs in linear time , 2006, KDD '06.

[36]  Cordelia Schmid,et al.  Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[37]  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).

[38]  Pietro Perona,et al.  Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[39]  Nitesh V. Chawla,et al.  SMOTEBoost: Improving Prediction of the Minority Class in Boosting , 2003, PKDD.

[40]  Foster J. Provost,et al.  Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction , 2003, J. Artif. Intell. Res..

[41]  Koby Crammer,et al.  On the Learnability and Design of Output Codes for Multiclass Problems , 2002, Machine Learning.

[42]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[43]  Nello Cristianini,et al.  Large Margin DAGs for Multiclass Classification , 1999, NIPS.

[44]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[45]  Ulrich H.-G. Kreßel,et al.  Pairwise classification and support vector machines , 1999 .

[46]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[47]  Laura Schweitzer,et al.  Advances In Kernel Methods Support Vector Learning , 2016 .

[48]  Matthijs C. Dorst Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[49]  Zhi-Hua Zhou,et al.  Exploratory Undersampling for Class-Imbalance Learning , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[50]  Thanh-Nghi Do,et al.  Using Local Node Information in Decision Trees: Coupling a Local Labeling Rule with an Off-centered Entropy , 2008, DMIN.

[51]  Ralescu Anca,et al.  ISSUES IN MINING IMBALANCED DATA SETS - A REVIEW PAPER , 2005 .

[52]  Gabriela Csurka,et al.  Visual categorization with bags of keypoints , 2002, eccv 2004.

[53]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[54]  Jason Weston,et al.  Support vector machines for multi-class pattern recognition , 1999, ESANN.

[55]  Vladimir Naumovich Vapni The Nature of Statistical Learning Theory , 1995 .

[56]  Message Passing Interface Forum MPI: A message - passing interface standard , 1994 .