Plant leaf images segmentation via graph diffusion process

Published: 13 July 2023| Version 1 | DOI: 10.17632/mwgvg882gb.1


We present the segmentation results obtained from our graph-based diffusion process using random walk with restart on a mono-layered graph using the public 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. The dataset comprises 233 high-resolution leaf images captured in their natural environment. The images include various artefacts that pose challenges to the segmentation task, such as shadows, varying illumination, and the presence of overlapping leaves. Our algorithm emphasizes the leaf parts by diffusing intensity scores from foreground templates towards image boundaries. The resulting saliency maps are further refined through a fusion process with saliency maps generated by random forests. The refined saliency maps are then thresholded to extract the leaves from their backgrounds. Ground truth images are available to visually evaluate 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. * DF_sal: contains the saliency maps derived from the diffusion process within the graph. * RF_sal: contains the saliency maps generated by random forests. * final_sal: contains the final saliency maps obtained after the fusion process. * PRE_segmentation: contains the segmentation results obtained after thresholding the final saliency map and before refinement. * final_segmentation: contains the final segmentation results obtained after refinement. *SLG_Segmentation results: a compressed folder containing the above folders



Universite Mouloud Mammeri de Tizi Ouzou


Image Segmentation, Precision Agriculture, Computer Vision Algorithms