Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Text classification using string kernels
The Journal of Machine Learning Research
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Protein function prediction via graph kernels
Bioinformatics
Optimal assignment kernels for attributed molecular graphs
ICML '05 Proceedings of the 22nd international conference on Machine learning
Graphical Models and Point Pattern Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semi-Supervised Learning (Adaptive Computation and Machine Learning)
Semi-Supervised Learning (Adaptive Computation and Machine Learning)
Edit distance-based kernel functions for structural pattern classification
Pattern Recognition
The Pyramid Match Kernel: Efficient Learning with Sets of Features
The Journal of Machine Learning Research
A structural alignment kernel for protein structures
Bioinformatics
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Indexing local configurations of features for scalable content-based video copy detection
LS-MMRM '09 Proceedings of the First ACM workshop on Large-scale multimedia retrieval and mining
Interactive learning of heterogeneous visual concepts with local features
Proceedings of the international conference on Multimedia
Geometry aware local kernels for object recognition
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
Superposition and Alignment of Labeled Point Clouds
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Weisfeiler-Lehman Graph Kernels
The Journal of Machine Learning Research
An efficient framework for constructing generalized locally-induced text metrics
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Entity disambiguation in anonymized graphs using graph kernels
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Geometric tree kernels: classification of COPD from airway tree geometry
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
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Point clouds are sets of points in two or three dimensions. Most kernel methods for learning on sets of points have not yet dealt with the specific geometrical invariances and practical constraints associated with point clouds in computer vision and graphics. In this paper, we present extensions of graph kernels for point clouds, which allow one to use kernel methods for such objects as shapes, line drawings, or any three-dimensional point clouds. In order to design rich and numerically efficient kernels with as few free parameters as possible, we use kernels between covariance matrices and their factorizations on probabilistic graphical models. We derive polynomial time dynamic programming recursions and present applications to recognition of handwritten digits and Chinese characters from few training examples.