Aletti Giacomo, Benfenati Alessandro, Naldi Giovanni
Environmental Science and Policy Department, Università degli Studi di Milano, 20133 Milan, Italy.
J Imaging. 2021 Oct 7;7(10):208. doi: 10.3390/jimaging7100208.
Image segmentation is an essential but critical component in low level vision, image analysis, pattern recognition, and now in robotic systems. In addition, it is one of the most challenging tasks in image processing and determines the quality of the final results of the image analysis. Colour based segmentation could hence offer more significant extraction of information as compared to intensity or texture based segmentation. In this work, we propose a new local or global method for multi-label segmentation that combines a random walk based model with a direct label assignment computed using a suitable colour distance. Our approach is a semi-automatic image segmentation technique, since it requires user interaction for the initialisation of the segmentation process. The random walk part involves a combinatorial Dirichlet problem for a weighted graph, where the nodes are the pixel of the image, and the positive weights are related to the distances between pixels: in this work we propose a novel colour distance for computing such weights. In the random walker model we assign to each pixel of the image a probability quantifying the likelihood that the node belongs to some subregion. The computation of the colour distance is pursued by employing the coordinates in a colour space (e.g., RGB, XYZ, YCbCr) of a pixel and of the ones in its neighbourhood (e.g., in a 8-neighbourhood). The segmentation process is, therefore, reduced to an optimisation problem coupling the probabilities from the random walker approach, and the similarity with respect the labelled pixels. A further investigation involves an adaptive preprocess strategy using a regression tree for learning suitable weights to be used in the computation of the colour distance. We discuss the properties of the new method also by comparing with standard random walk and k-means approaches. The experimental results carried on the White Blood Cell (WBC) dataset and GrabCut datasets show the remarkable performance of the proposed method in comparison with state-of-the-art methods, such as normalised random walk and normalised lazy random walk, with respect to segmentation quality and computational time. Moreover, it reveals to be very robust with respect to the presence of noise and to the choice of the colourspace.
图像分割是低级视觉、图像分析、模式识别以及当前机器人系统中一个重要但关键的组成部分。此外，它是图像处理中最具挑战性的任务之一，并且决定了图像分析最终结果的质量。因此，与基于强度或纹理的分割相比，基于颜色的分割可以提供更显著的信息提取。在这项工作中，我们提出了一种新的用于多标签分割的局部或全局方法，该方法将基于随机游走的模型与使用合适颜色距离计算的直接标签分配相结合。我们的方法是一种半自动图像分割技术，因为它需要用户交互来初始化分割过程。随机游走部分涉及加权图的组合狄利克雷问题，其中节点是图像的像素，正权重与像素之间的距离相关：在这项工作中，我们提出了一种用于计算此类权重的新颖颜色距离。在随机游走模型中，我们为图像的每个像素分配一个概率，该概率量化了该节点属于某个子区域的可能性。颜色距离的计算是通过使用像素及其邻域（例如在8邻域中）在颜色空间（例如RGB、XYZ、YCbCr）中的坐标来进行的。因此，分割过程简化为一个优化问题，该问题将随机游走方法的概率与相对于标记像素的相似度耦合起来。进一步的研究涉及一种自适应预处理策略，该策略使用回归树来学习在颜色距离计算中使用的合适权重。我们还通过与标准随机游走和k均值方法进行比较来讨论新方法的特性。在白细胞（WBC）数据集和GrabCut数据集上进行的实验结果表明，与归一化随机游走和归一化懒惰随机游走等现有方法相比，所提出的方法在分割质量和计算时间方面具有显著性能。此外，它对于噪声的存在和颜色空间的选择非常稳健。
