Learning on graphs and hierarchies from the multimedia perspective

Key words : learning on hierarchical data, image segmentation, edge detection

Hierarchies, as described in mathematical morphology, represent nested regions of interest and provide mechanisms to create concepts and coherent data organization. They facilitate high-level analysis and management of large amounts of data. Represented as hierarchical trees, they have formalisms intersecting with graph theory and applications that can be conveniently generalized.

This work aims to create a learning framework that can operate with hierarchical data and is agnostic to the input and the application. The idea is to study ways to transform the data to a regular representation required by most learning models while preserving the rich information in the hierarchical structure. It proposes to study and formalize the concepts as graphs, a common point for hierarchies and multimedia, and a topic of great interest for machine learning.

The methods in this study use edge-weighted image graphs and hierarchical trees as input, evaluating different proposals on the edge detection and segmentation tasks.

Furthermore, it provides a critical systematic review of proposals in the literature that integrates machine learning and hierarchies. It demonstrates that it is possible to create a learning framework dependent only on the hierarchical data that performs well in multiple tasks with different models.