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Andrea Vedaldi, Ph.D. Junior Research Fellow, New College Oxford Oxford Visual Geometry Group (directions) vedaldi@robots.ox.ac.uk Tel. +44 1865 283057 Résumé Google Scholar |
I am a research fellow with the Oxford VGG group. My research interests include machine learning and invariant visual representations with applications to the classification and detection of object categories. I am one of the main authors of the VLFeat library.
- 10/1/2012
- PASCAL Harvest grant for VLFeat development.
- 11/12/2011
- svm-struct-matlab 1.1 adds support for Windows (thansk to Iasonas Kokkinos!).
- 10/5/2011
- svm-struct-matlab 1.0 released! This new project is a MATLAB wrapper of SVMstruct.
- 10/22/2010
- VLFeat wins the ACM Multimedia Open Source Software Competition
- 9/5/2010
- VLFeat presented at the ECCV10 Tutorial on Computer Vision and 3D Perception for Robotics
- 6/11/2010
- CVPR10 Tutorial on Open Source Vision Software.
- 6/11/2010
- New contributed Python interface to siftpp.
Research highlights
ICCV 2011 Poster
Paper
Learning Equivariant Structured Output SVM Regressors. We introcude a method to learn equivariant functions with Supprot Vector Machines (SVMs). Examples include: a transformation-invariant multi-class classifier, learning to detect a rotating object without searching for the rotation, and learning to rank images of pedestrians invariantly to jitter and articulation.
CVPR 2010 Poster
Paper
Efficient additive kernels: The homogeneous kernel map. We introduce closed-form finite dimensional feature maps approximating the additive kernels (intersection, Hellinger’s, χ2, Jensen-Shannon, ...). By adding onle line to your code you can use non-linear additive kernels as if they were linear, with vastly improved training and testing speed and compactness of the resulting models (code).