Large scale classifiers for visual classification tasks

Berg A, Deng J, Li FF (2010) Large scale visual recognition challenge 2010. Tech rep. http://www.image-net.org/challenges/LSVRC/2010/index Chawla NV, Lazarevic A, Hall LO, Bowyer KW (2003) Smoteboost: improving prediction of the minority class in boosting. In: The principles of knowledge discovery in databases, pp 107–119 Crammer K, Singer Y (2002) On the learnability and design of output codes for multiclass problems. Mach Learn 47(2–3):201–233 Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, Cambridge Csurka G, Dance CR, Fan L, Willamowski J, Bray C (2004) Visual categorization with bags of keypoints. In: Workshop on statistical learning in computer vision, European conference on computer vision, pp 1–22 Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: IEEE computer society conference on computer vision and pattern recognition. IEEE Computer Society, pp 886–893 Deng J, Berg AC, Li K, Li FF (2010) What does classifying more than 10,000 image categories tell us? In: European conference on computer vision, pp 71–84 Deng J, Dong W, Socher R, Li LJ, Li K, Li FF (2009) Imagenet: a large-scale hierarchical image database. In: IEEE computer society conference on computer vision and pattern recognition, pp 248–255 Do TN, Nguyen VH, Poulet F (2008) Speed up svm algorithm for massive classification tasks. In: International conference on advanced data mining and applications, pp 147–157 Doan TN, Do TN, Poulet F (2013) Large scale visual classification with parallel, imbalanced bagging and incremental liblinear svm. In: 9th international conference on data mining. Las Vegas, Nevada, pp 197–203 Doan TN, Do TN, Poulet F (2013) Parallel incremental svm for classifying million images with very high-dimensional signatures into thousand classes. In: IEEE international joint conference on neural networks. Dallas, pp 2976–2983 Duda RO, Hart PE (1973) Pattern classification and scene analysis. Wiley, New York Everingham M, Van Gool L, Williams CKI, Winn J, Zisserman A (2010) The pascal visual object classes (voc) challenge. Int J Comput Vis 88(2):303–338 Fergus R, Weiss Y, Torralba A (2009) Semi-supervised learning in gigantic image collections. In: Advances in neural information processing systems, pp 522–530 Griffin G, Holub A, Perona P (2007) Caltech-256 object category dataset. Tech. Rep. CNS-TR-2007-001, California Institute of Technology. http://authors.library.caltech.edu/7694 Griffin G, Perona D (2008) Learning and using taxonomies for fast visual categorization. In: IEEE computer society conference on computer vision and pattern recognition. IEEE Computer Society Guermeur Y (2007) Svm multiclasses, théorie et applications Hsieh CJ, Chang KW, Lin CJ, Keerthi SS, Sundararajan S (2008) A dual coordinate descent method for large-scale linear svm. In: International conference on machine learning, pp 408–415 Japkowicz N (ed) (2000) AAAI’Workshop on learning from imbalanced data sets, no. WS-00-05 in AAAI Tech Report Joachims T (2006) Training linear svms in linear time. In: Proceedings of the ACM SIGKDD international conference on knowledge discovery. ACM, pp 217–226 Keerthi SS, Sundararajan S, Chang KW, Hsieh CJ, Lin CJ (2008) A sequential dual method for large scale multi-class linear svms. In: KDD, pp 408–416 Krebel U (1999) Pairwise classification and support vector machines. In: Advances in kernel methods: support vector learning, pp 255–268 Lazebnik S, Schmid C, Ponce J (2006) Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: IEEE computer society conference on computer vision and pattern recognition, pp 2169–2178 Lenca P, Lallich S, Do TN, Pham NK (2008) A comparison of different off-centered entropies to deal with class imbalance for decision trees. In: The Pacific-Asia conference on knowledge discovery and data mining, LNAI 5012. Springer, pp 634–643 Li FF, Fergus R, Perona P (2007) Learning generative visual models from few training examples: an incremental bayesian approach tested on 101 object categories. Comput Vis Image Underst 106(1):59–70 Li Y, Crandall DJ, Huttenlocher DP (2009) Landmark classification in large-scale image collections. In: IEEE 12th international conference on computer vision. IEEE, pp 1957–1964 Lin CJ, Weng RC, Keerthi SS (2008) Trust region newton method for logistic regression. J Mach Learn Res 9:627–650 Lin Y, Lv F, Zhu S, Yang M, Cour T, Yu K, Cao L, Huang TS (2011) Large-scale image classification: Fast feature extraction and svm training. In: IEEE computer society conference on computer vision and pattern recognition, pp 1689–1696 Liu XY, Wu J, Zhou ZH (2009) Exploratory undersampling for class-imbalance learning. IEEE Trans Syst Man Cybern Part B 39(2):539–550 Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110 Maji S, Berg AC, Malik J (2008) Classification using intersection kernel support vector machines is efficient. In: IEEE computer society conference on computer vision and pattern recognition Maji S, Berg AC, Malik J (2013) Efficient classification for additive kernel svms. IEEE Trans Pattern Anal Mach Intell 35(1):66–77 MPI-Forum: Mpi: a message-passing interface standard. http://www.mpi-forum.org OpenMP Architecture Review Board: OpenMP application program interface version 3.0. http://www.openmp.org/mp-documents/spec30.pdf (2008) Perronnin F, Akata Z, Harchaoui Z, Schmid C (2012) Towards good practice in large-scale learning for image classification. In: IEEE computer society conference on computer vision and pattern recognition, pp 3482–3489 Perronnin F, Sánchez J, Liu Y (2010) Large-scale image categorization with explicit data embedding. In: IEEE computer society conference on computer vision and pattern recognition, pp 2297–2304 Pham NK, Do TN, Lenca P, Lallich S (2008) Using local node information in decision trees: coupling a local decision rule with an off-centered entropy. In: International conference on data mining. CSREA Press, Las Vegas, pp 117–123 Platt J, Cristianini N, Shawe-Taylor J (2000) Large margin dags for multiclass classification. Adv Neural Inf Process Syst 12:547–553 Ricamato MT, Marrocco C, Tortorella F (2008) Mcs-based balancing techniques for skewed classes: an empirical comparison. In: International conference on pattern recognition, pp 1–4 Sánchez J, Perronnin F (2011) High-dimensional signature compression for large-scale image classification. In: IEEE computer society conference on computer vision and pattern recognition, pp 1665–1672 Shalev-Shwartz S, Singer Y, Srebro N (2007) Pegasos: primal estimated sub-gradient solver for svm. In: International conference on machine learning, pp 807–814 Suykens JAK, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9(3):293–300. doi:10.1023/A:1018628609742 Vapnik V (1995) The nature of statistical learning theory. Springer, Berlin Vedaldi A, Gulshan V, Varma M, Zisserman A (2009) Multiple kernels for object detection. In: IEEE 12th international conference on computer vision. IEEE, pp 606–613 Vedaldi A, Zisserman A (2012) Efficient additive kernels via explicit feature maps. IEEE Trans Pattern Anal Mach Intell 34(3):480–492 Visa S, Ralescu A (2005) Issues in mining imbalanced data sets–a review paper. In: Midwest artificial intelligence and cognitive science conf. Dayton, pp 67–73 Wang C, Yan S, Zhang HJ (2009) Large scale natural image classification by sparsity exploration. In: Proceedings of the IEEE international conference on acoustics, speech, and signal processing. IEEE, pp 3709–3712 Weiss GM, Provost F (2003) Learning when training data are costly: the effect of class distribution on tree induction. J Artif Intell Res 19:315–354 Weston J, Watkins C (1999) Support vector machines for multi-class pattern recognition. In: Proceedings of the seventh European symposium on artificial neural networks, pp 219–224 Wu J (2010) A fast dual method for hik svm learning. In: Daniilidis K, Maragos P, Paragios N (eds) European conference on computer vision, lecture notes in computer science, vol 6312. Springer, Berlin, pp 552–565 Wu J (2012) Power mean svm for large scale visual classification. In: IEEE computer society conference on computer vision and pattern recognition, pp 2344–2351 Wu J, Tan WC, Rehg JM (2011) Efficient and effective visual codebook generation using additive kernels. J Mach Learn Res 12:3097–3118 Yu HF, Hsieh CJ, Chang KW, Lin CJ (2012) Large linear classification when data cannot fit in memory. ACM Trans Knowl Discov Data 5(4):23 Yu K, Zhang T, Gong Y (2009) Nonlinear learning using local coordinate coding. In: Advances in neural information processing systems, pp 2223–2231 Yuan GX, Ho CH, Lin CJ (2012) Recent advances of large-scale linear classification. Proc IEEE 100(9):2584–2603 Zhou X, Yu K, Zhang T, Huang TS (2010) Image classification using super-vector coding of local image descriptors. In: European conference on computer vision, pp 141–154