International Journal of Computer Vision
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Pegasos: Primal Estimated sub-GrAdient SOlver for SVM
Proceedings of the 24th international conference on Machine learning
A dual coordinate descent method for large-scale linear SVM
Proceedings of the 25th international conference on Machine learning
Building kernels from binary strings for image matching
IEEE Transactions on Image Processing
Balance support vector machines locally using the structural similarity kernel
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
Specific vehicle detection and tracking in road environment
Proceedings of the Third International Conference on Internet Multimedia Computing and Service
A machine learning approach to tongue motion analysis in 2D ultrasound image sequences
MLMI'11 Proceedings of the Second international conference on Machine learning in medical imaging
Efficient and Effective Visual Codebook Generation Using Additive Kernels
The Journal of Machine Learning Research
Discriminative compact pyramids for object and scene recognition
Pattern Recognition
Scene categorization based on integrated feature description and local weighted feature mapping
Computers and Electrical Engineering
A hierarchical clustering based non-maximum suppression method in pedestrian detection
IScIDE'11 Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Large-scale gaussian process classification with flexible adaptive histogram kernels
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
A kernel-based framework for image collection exploration
Journal of Visual Languages and Computing
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
Rapid uncertainty computation with gaussian processes and histogram intersection kernels
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
Reduced heteroscedasticity linear regression for Nyström approximation
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Histograms are used in almost every aspect of computer vision, from visual descriptors to image representations. Histogram Intersection Kernel (HIK) and SVM classifiers are shown to be very effective in dealing with histograms. This paper presents three contributions concerning HIK SVM classification. First, instead of limited to integer histograms, we present a proof that HIK is a positive definite kernel for non-negative real-valued feature vectors. This proof reveals some interesting properties of the kernel. Second, we propose ICD, a deterministic and highly scalable dual space HIK SVM solver. ICD is faster than and has similar accuracies with general purpose SVM solvers and two recently proposed stochastic fast HIK SVM training methods. Third, we empirically show that ICD is not sensitive to the C parameter in SVM. ICD achieves high accuracies using its default parameters in many datasets. This is a very attractive property because many vision problems are too large to choose SVM parameters using cross-validation.