Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Using the Fisher Kernel Method to Detect Remote Protein Homologies
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
On-Line Handwriting Recognition with Support Vector Machines " A Kernel Approach
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Journal of Machine Learning Research
Recognition with Local Features: the Kernel Recipe
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
A survey of kernels for structured data
ACM SIGKDD Explorations Newsletter
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
PLSA-based image auto-annotation: constraining the latent space
Proceedings of the 12th annual ACM international conference on Multimedia
Formulating Semantic Image Annotation as a Supervised Learning Problem
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Mercer Kernels for Object Recognition with Local Features
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
The Pyramid Match Kernel: Efficient Learning with Sets of Features
The Journal of Machine Learning Research
Multiscale conditional random fields for image labeling
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Context-Dependent Kernels for Object Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometry aware local kernels for object recognition
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
Probabilistic spatial context models for scene content understanding
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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In kernel methods, such as support vector machines, many existing kernels consider similarity between data by taking into account only their content and without context. In this paper, we propose an alternative that upgrades and further enhances usual kernels by making them context-aware. The proposed method is based on the optimization of an objective function mixing content, regularization and also context. We will show that the underlying kernel solution converges to a positive semi-definite similarity, which can also be expressed as a dot product involving "explicit" kernel maps. When combining these context-aware kernels with support vector machines, performances substantially improve for the challenging task of image annotation.