A Subpath Kernel for Learning Hierarchical Image Representations

TL;DR

This paper introduces a new subpath kernel designed for hierarchical image data, especially in remote sensing, improving classification by effectively handling unordered trees with numerical features.

Contribution

It presents a novel structured kernel tailored for unordered hierarchical image representations, extending the subpath kernel concept to numerical node features.

Findings

Improved classification accuracy on artificial datasets

Enhanced remote sensing image classification results

Effective handling of unordered tree structures

Abstract

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