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Tree kernels have demonstrated their ability to deal with hierarchical data,as the intrinsic tree structure often plays a discriminative role. While suchkernels have been successfully applied to various domains such as naturelanguage processing and bioinformatics, they mostly concentrate on orderedtrees and whose nodes are described by symbolic data. Meanwhile, hierarchicalrepresentations have gained increasing interest to describe image content. Thisis particularly true in remote sensing, where such representations allow forrevealing different objects of interest at various scales through a treestructure. However, the induced trees are unordered and the nodes are equippedwith numerical features. In this paper, we propose a new structured kernel forhierarchical image representations which is built on the concept of subpathkernel. Experimental results on both artificial and remote sensing datasetsshow that the proposed kernel manages to deal with the hierarchical nature ofthe data, leading to better classification rates.

A Subpath Kernel for Learning Hierarchical Image Representations