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Relevance model based image segmentation

机译:基于相关模型的图像分割

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Image segmentation is a fundamental process in computer vision applications. This paper presents a novel method to deal with the issue of image segmentation. Each image is first segmented coarsely, and represented as a graph model. Then, a semi-supervised algorithm is utilized to estimate the relevance between labeled nodes and unlabeled nodes to construct a relevance matrix. Finally, a normalized cut criterion is utilized to segment images into meaningful units. The experimental results conducted on Berkeley image databases and MSRC image databases demonstrate the effectiveness of the proposed strategy.
机译:图像分割是计算机视觉应用程序中的基本过程。本文提出了一种新的方法来处理图像分割问题。首先将每个图像粗略地分割,并表示为图形模型。然后,使用半监督算法估计标记节点和未标记节点之间的相关性,以构建相关性矩阵。最后,利用归一化的剪切标准将图像分割成有意义的单位。在伯克利图像数据库和MSRC图像数据库上进行的实验结果证明了该策略的有效性。

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