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

We showcase the results of our graph-based diffusion technique utilizing random walks with restarts on a multi-layered graph using the publicly accessible 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 comprises 233 high-resolution leaf images taken in their natural environments, presenting various segmentation challenges such as shadows, diverse lighting conditions, and leaf overlap. Our method primarily focuses on identifying leaf regions by initially locating the leaves within the images and then propagating intensity scores from foreground templates to image boundaries to generate saliency maps. By applying a threshold to these saliency maps produced through the diffusion process, we derive binary masks that effectively separate the leaves from the backgrounds. Ground truth images are provided for visual evaluation of our algorithm's performance.Folders description: image: RGB images mask: Ground truth masks FG_templates: foreground templates and bounding boxes defined on dataset images
Salinecy_map: saliency maps obtained by our approach PR_masks: Predicted masks obtained by tresholding our salinecy maps Plant_Leaf_Segmentation: a compressed folder containing the above folders.

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.