Robust random walk for leaf segmentation

In this study, the authors focus on the task of leaf segmentation under different imaging conditions (e.g. backgrounds and shadows). A new method ‐ robust random walk (RW) is proposed to propagate the prior of user's specified pixels. Specifically, they first employ RWs to take the relationship of pairwise pixels into consideration. A superpixel‐consistent constraint is added to make the edges of segmentation smooth. Owing to the effect of illumination, some parts of a leaf surface are brighter than others and it may further harm the subsequent label propagation. To address this problem, they learn a common subspace by taking into account the illumination of local and non‐local pixels. By doing so, it has good adaptability to process noise interfering and non‐uniform illumination. In addition, since RW only considers the pairwise relationship of pixels, it will be sensitive to the specified and connected pixels. Thus, they further employ a log‐likelihood ratio to predict the probability of a pixel belonging to the background and use it to guide the label propagation. Based on the proposed method, they can obtain a smoothed and robust leaf segmentation. Experimental results on unconstrained leaf images demonstrate the efficiency of their algorithm.