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GBRPR
2007
Springer

Image Classification Using Marginalized Kernels for Graphs

9 years 6 months ago
Image Classification Using Marginalized Kernels for Graphs
We propose in this article an image classification technique based on kernel methods and graphs. Our work explores the possibility of applying marginalized kernels to image processing. In machine learning, performant algorithms have been developed for data organized as real valued arrays; these algorithms are used for various purposes like classification or regression. However, they are inappropriate for direct use on complex data sets. Our work consists of two distinct parts. In the first one we model the images by graphs to be able to represent their structural properties and inherent attributes. In the second one, we use kernel functions to project the graphs in a mathematical space that allows the use of performant classification algorithms. Experiments are performed on medical images acquired with various modalities and concerning different parts of the body.
Emanuel Aldea, Jamal Atif, Isabelle Bloch
Added 16 Aug 2010
Updated 16 Aug 2010
Type Conference
Year 2007
Where GBRPR
Authors Emanuel Aldea, Jamal Atif, Isabelle Bloch
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