Felipe A.S. Kleine, Luiz F.D. Santos, Fábio A.M. Cappabianco, Paulo A.V. Miranda
Unsupervised Image Segmentation by Oriented Image Foresting Transform in Layered Graphs,
36th Conference on Graphics, Patterns and Images (SIBGRAPI).
Nov 2023, Rio Grande, RS, Brazil, accepted, to appear.

Abstract

In this work, we address the problem of unsupervised image segmentation, subject to high-level constraints expected for the objects of interest. More specifically, we handle the segmentation of a hierarchy of objects with nested boundaries, each with its own expected boundary polarity constraint. To this end, this work successfully extends Hierarchical Layered Oriented Image Foresting Transform (HLOIFT), with the inclusion of nested object relations, to the unsupervised segmentation paradigm. On the other hand, this work can also be seen as an extension of Unsupervised OIFT (UOIFT) to include structural relationships of nested objects. The method is demonstrated in the segmentation of three datasets of colored images with superior performance compared to other existing techniques in graphs, requiring a smaller number of connected partitions to isolate the objects of interest in the images.

The method is demonstrated in the segmentation of 146 colored images, which contain objects with nested boundaries with ground truth for the innermost object, divided into three categories:

  1. Stop signs written in Portuguese to segment the four letters of the word "PARE" (Figures 1a e 1d). This dataset of 15 images is available here.
  2. QR codes to segment the center squares of the three position markers (Figures 1b e 1e). This dataset of 70 images is available here.
  3. State flags of São Paulo to segment the geographic silhouette of Brazil inside the white circle in their upper left corner (Figures 1c e 1f). This dataset of 61 images is available here.

Below are some sample images of the datasets:

(a) Image A(b) Image B(c) Image C
(d) Ground truth of image A(e) Ground truth of image B(f) Ground truth of image C
Figure 1: Sample images with 640 × 480 pixels and their ground truths.