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

Published: 13 July 2023| Version 1 | DOI: 10.17632/wpr3n7f4rf.1


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. Ground truth images are provided to visually assess the effectiveness of our algorithm's performance. Folders description: * JPEGimages: Leaf color images. * masks: The ground truth binary masks that accurately delineat the leaf regions. * foreground_template: contains the bounding boxes that localize the leaves in blue and the foreground templates in red drawn on dataset images. * saliency_maps: contains saliency maps obtained by diffusing foreground queries within a multi-layer graph. * segmentation_results : contains the segmentation results obtained after thresholding the saliency maps. *MLG_Segmentation_results: a compressed folder containing the above folders.



Universite Mouloud Mammeri de Tizi Ouzou


Image Segmentation, Precision Agriculture, Computer Vision Algorithms