A rapid flower/leaf recognition system
Proceedings of the 20th ACM international conference on Multimedia
Structured image segmentation using kernelized features
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Mixture component identification and learning for visual recognition
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Hybrid pooling fusion in the bow pipeline
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
Enhanced representation and multi-task learning for image annotation
Computer Vision and Image Understanding
Pooling in image representation: The visual codeword point of view
Computer Vision and Image Understanding
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
VISOR: towards on-the-fly large-scale object category retrieval
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Large scale visual classification with many classes
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
Exploring STIP-based models for recognizing human interactions in TV videos
Pattern Recognition Letters
Human interaction categorization by using audio-visual cues
Machine Vision and Applications
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Large scale nonlinear support vector machines (SVMs) can be approximated by linear ones using a suitable feature map. The linear SVMs are in general much faster to learn and evaluate (test) than the original nonlinear SVMs. This work introduces explicit feature maps for the additive class of kernels, such as the intersection, Hellinger's, and χ虏 kernels, commonly used in computer vision, and enables their use in large scale problems. In particular, we: 1) provide explicit feature maps for all additive homogeneous kernels along with closed form expression for all common kernels; 2) derive corresponding approximate finite-dimensional feature maps based on a spectral analysis; and 3) quantify the error of the approximation, showing that the error is independent of the data dimension and decays exponentially fast with the approximation order for selected kernels such as χ虏. We demonstrate that the approximations have indistinguishable performance from the full kernels yet greatly reduce the train/test times of SVMs. We also compare with two other approximation methods: Nystrom's approximation of Perronnin et al. [1], which is data dependent, and the explicit map of Maji and Berg [2] for the intersection kernel, which, as in the case of our approximations, is data independent. The approximations are evaluated on a number of standard data sets, including Caltech-101 [3], Daimler-Chrysler pedestrians [4], and INRIA pedestrians [5].