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Image Labeling by Integrating Global Information by 7 Patches and Local Information

机译:通过将全局信息集成为7个修补程序和本地信息来标记

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We propose an image labeling method by integrating the probabilities of local and global information. Many conventional methods put label to each pixel or region by using the features extracted from local regions and local contextual relationships between neighboring regions. However, labeling results tend to depend on a local viewpoint. To overcome this problem, we propose the image labeling method using not only local information but also global information. The probability by global information is estimated by KNearest Neighbor. In the experiments using the MSRC21 dataset, labeling accuracy is much improved by using global information.
机译:我们通过集成本地和全局信息的概率来提出图像标记方法。许多传统方法通过使用从局域区域提取的特征和相邻区域之间的本地上下文关系来将标签放置到每个像素或区域。但是,标记结果往往取决于局部观点。为了克服这个问题,我们提出了不仅使用本地信息的图像标记方法,还提出了全局信息。通过拐弯邻估计全球信息的概率。在使用MSRC21数据集的实验中,使用全局信息,标记精度大大提高。

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