Large-scale visual concept detection with explicit kernel maps and power mean SVM
Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
Exclusive visual descriptor quantization
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Object templates for visual place categorization
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part IV
Large scale visual classification with many classes
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
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PmSVM (Power Mean SVM), a classifier that trains significantly faster than state-of-the-art linear and non-linear SVM solvers in large scale visual classification tasks, is presented. PmSVM also achieves higher accuracies. A scalable learning method for large vision problems, e.g., with millions of examples or dimensions, is a key component in many current vision systems. Recent progresses have enabled linear classifiers to efficiently process such large scale problems. Linear classifiers, however, usually have inferior accuracies in vision tasks. Non-linear classifiers, on the other hand, may take weeks or even years to train. We propose a power mean kernel and present an efficient learning algorithm through gradient approximation. The power mean kernel family include as special cases many popular additive kernels. Empirically, PmSVM is up to 5 times faster than LIBLINEAR, and two times faster than state-of-the-art additive kernel classifiers. In terms of accuracy, it outperforms state-of-the-art additive kernel implementations, and has major advantages over linear SVM.