A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
The nature of statistical learning theory
The nature of statistical learning theory
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
An updated set of basic linear algebra subprograms (BLAS)
ACM Transactions on Mathematical Software (TOMS)
Support Vector Machine Active Learning with Application sto Text Classification
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Classifying large data sets using SVMs with hierarchical clusters
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
A support vector method for multivariate performance measures
ICML '05 Proceedings of the 22nd international conference on Machine learning
Large Scale Classification with Support Vector Machine Algorithms
ICMLA '07 Proceedings of the Sixth International Conference on Machine Learning and Applications
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We are interested in varying the vocabulary size in the image categorization task with a bag-of-visual-words to investigate its influence on the classification accuracy in two cases: in the first one, both the test-set and the training set contains the same objects (with only different view points in the test-set) and the second one where objects in the test-set do not appear at all in the training set (only other objects from the same category appear). In order to perform these tasks, we need to scale-up the algorithms used to deal with millions data points in hundred of thousand dimensions. We present k-means (used in the quantization step) and SVM (used in the classification step) algorithms extended to deal with very large datasets. These new incremental and parallel algorithms can be used on various distributed architectures, like multithread computer, cluster or GPU (graphics processing units). The efficiency of the approach is shown with the categorization of the 3D-Dataset from Savarese and Fei-Fei containing about 6700 images of 3D objects from 10 different classes. The obtained incremental and parallel SVM algorithm is several orders of magnitude faster than usual ones (like lib-SVM, SVM-perf or CB-SVM) and the incremental and parallel k-means is at least one order of magnitude faster than usual implementations.