Graph Learning as a Basis for Image Segmentation

Abstract: Graph signal processing is a field concerning theprocessing of graphs with data associated to their vertices, oftenin the purpose of modeling networks. One area of this fieldthat has been under research in recent years is the developmentof frameworks for learning graph topologies from such data.This may be useful in situations where one wants to representa phenomenon with a graph, but where an obvious topologyis not available. The aim of this project was to evaluate theusefulness of one such proposed learning framework in thecontext of image segmentation. The method used for achievingthis consisted in constructing graph representations of imagesfrom said framework, and clustering their vertices with anestablished graph-based segmentation algorithm. The resultsdemonstrate that this approach may well be useful, although theimplementation used in the project carried out segmentationssignificantly slower than state of the art methods. A numberof possible improvements to be made regarding this aspect arehowever pointed out and may be subject for future work.