Characteristic kernels on structured domains excel in robotics and human action recognition

Authors:
Somayeh Danafar;Arthur Gretton;Jürgen Schmidhuber
Affiliations:
Istituto Dalle Molle di Studi sull'Intelligenza Artificiale, Manno-Lugano, Switzerland;Carnegie Mellon University, Pittsburgh and MPI for Biological Cybernetics, Tübingen, Germany;Istituto Dalle Molle di Studi sull'Intelligenza Artificiale, Manno-Lugano, Switzerland
Venue:
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Year:
2010

Citing 6
Cited 0

Dimensionality Reduction for Supervised Learning with Reproducing Kernel Hilbert Spaces

The Journal of Machine Learning Research
Recognizing Human Actions: A Local SVM Approach

ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)

Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words

International Journal of Computer Vision
A Hilbert Space Embedding for Distributions

ALT '07 Proceedings of the 18th international conference on Algorithmic Learning Theory
Action recognition for surveillance applications using optic flow and SVM

ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

Embedding probability distributions into a sufficiently rich (characteristic) reproducing kernel Hilbert space enables us to take higher order statistics into account. Characterization also retains effective statistical relation between inputs and outputs in regression and classification. Recent works established conditions for characteristic kernels on groups and semigroups. Here we study characteristic kernels on periodic domains, rotation matrices, and histograms. Such structured domains are relevant for homogeneity testing, forward kinematics, forward dynamics, inverse dynamics, etc. Our kernel-based methods with tailored characteristic kernels outperform previous methods on robotics problems and also on a widely used benchmark for recognition of human actions in videos.