Superpixel-Classifikation with Graph Neural Networks

12 October 2021, by R. Johanson

Photo: Uriel SC

The goal of this project is to build a new superpixel classifier for the superpixel-based object proposal refinement system that was developed by Wilms and Frintrop.

The task of object proposal generation or object discovery is a fundamental step of many state-of-the-art object detection systems because it enables them to focus their attention on the image regions that are most likely to contain objects. One way to propose objects is as a pixel-precise segmentation mask that shows the object’s location on the input image and, ideally, perfectly adheres to the object boundaries. However, the latter is not easy to achieve since the segmentation masks are, in most cases, generated from highly downscaled versions of the input and therefore do not contain detailed information about object boundaries.

Participants: R. Johanson