Plant leaf images segmentation through multi-layer graph diffusion process.

We demonstrate the outcomes of our graph-based diffusion method that employs random walk with restart on a multi-layered graph using the publicly available Pl@ntleaves (H. Go¨eau, P. Bonnet, A. Joly, N. Boujemaa, D. Barth´el´emy, J.-F. Molino, P. Birnbaum, E. Mouysset, M. Picard, The clef 2011 plant images classification task, Vol. 1177, 2011.) dataset. This dataset consists of 233 high-resolution leaf images captured in their natural surroundings.

The images present various challenges for segmentation, including shadows, varying lighting conditions, and overlapping leaves. Our algorithm focuses on leaf portions by spreading intensity scores from foreground templates to image boundaries. By applying a threshold to the saliency maps generated through the diffusion process, we obtain binary masks that separate the leaves from the backgrounds.