Abstract
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.
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Aldea, E., Fouquier, G., Atif, J., Bloch, I. (2007). Kernel Fusion for Image Classification Using Fuzzy Structural Information. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2007. Lecture Notes in Computer Science, vol 4842. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76856-2_30
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DOI: https://doi.org/10.1007/978-3-540-76856-2_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76855-5
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