Fuzzy Sets and Systems - Special issue on fuzzy methods for computer vision and pattern recognition
Towards general measures of comparison of objects
Fuzzy Sets and Systems - Special issue dedicated to the memory of Professor Arnold Kaufmann
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Extensions of marginalized graph kernels
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Discovering Objects and their Localization in Images
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Fuzzy spatial relationships for image processing and interpretation: a review
Image and Vision Computing
Image classification using marginalized kernels for graphs
GbRPR'07 Proceedings of the 6th IAPR-TC-15 international conference on Graph-based representations in pattern recognition
A random walk kernel derived from graph edit distance
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Edit distance based kernel functions for attributed graph matching
GbRPR'05 Proceedings of the 5th IAPR international conference on Graph-Based Representations in Pattern Recognition
Hierarchical watersheds within the combinatorial pyramid framework
DGCI'05 Proceedings of the 12th international conference on Discrete Geometry for Computer Imagery
Hybrid image segmentation using watersheds and fast region merging
IEEE Transactions on Image Processing
Support vector machines for histogram-based image classification
IEEE Transactions on Neural Networks
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Various kernel functions on graphs have been defined recently. In this article, our purpose is to assess the efficiency of a marginalized kernel for image classification using structural information. Graphs are built from image segmentations, and various types of information concerning the underlying image regions as well as the spatial relationships between them are incorporated as attributes in the graph labeling. The main contribution of this paper consists in studying the impact of fusioning kernels for different attributes on the classification decision, while proposing the use of fuzzy attributes for estimating spatial relationships.