论文标题
建立用于使用粒子竞争与合作的图像分割的网络
Building Networks for Image Segmentation using Particle Competition and Cooperation
论文作者
论文摘要
粒子竞争与合作（PCC）是一种基于图的半监督学习方法。当将PCC应用于交互式图像分割任务时，像素将转换为网络节点，并根据从图像中提取的一组功能之间的距离，将每个节点连接到其k-nearthiment邻居。建立适当的网络来喂养PCC对于获得良好的分割结果至关重要。但是，根据要分割的图像的特征，某些功能可能比其他特征更重要以识别细分。在本文中，提出了评估候选网络的索引。因此，构建网络成为基于提议的索引优化某些特征权重的问题。计算机仿真是在Microsoft GrabCut数据库中的一些现实世界图像上执行的，本文中相关的分割结果显示了该方法的有效性。
Particle competition and cooperation (PCC) is a graph-based semi-supervised learning approach. When PCC is applied to interactive image segmentation tasks, pixels are converted into network nodes, and each node is connected to its k-nearest neighbors, according to the distance between a set of features extracted from the image. Building a proper network to feed PCC is crucial to achieve good segmentation results. However, some features may be more important than others to identify the segments, depending on the characteristics of the image to be segmented. In this paper, an index to evaluate candidate networks is proposed. Thus, building the network becomes a problem of optimizing some feature weights based on the proposed index. Computer simulations are performed on some real-world images from the Microsoft GrabCut database, and the segmentation results related in this paper show the effectiveness of the proposed method.
