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An Object Segmentation Method Based on Saliency Map and Spectral Clustering

机译:一种基于显着图和光谱聚类的对象分割方法

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Image segmentation is an important research topic in the field of computer vision. Spectral Clustering (SC) algorithm is one of the most popular used clustering methods for image segmentation. However, the cluster number must be estimated by expertise users to be determined. This limits its application in image segmentation. In this paper, we proposed an image segmentation method based on saliency map and spectral clustering algorithm (SC) that includes the initialization and determination of the number of cluster k. Quantitative and qualitative experimental evaluation on MSRA-1000 public image dataset depicts the promising results from the proposed method by outperforming four state of the art image segmentation methods.
机译:图像分割是计算机愿景领域的重要研究主题。光谱聚类(SC)算法是图像分割最受欢迎的使用聚类方法之一。但是,必须由要确定的专业知识估算群集号码。这限制了其在图像分割中的应用。在本文中,我们提出了一种基于显着图的图像分割方法和谱聚类算法(SC),包括初始化和确定簇K的数量。 MSRA-1000公共图像数据集的定量和定性实验评估描述了通过优于艺术图像分割方法的四个状态来实现所提出的方法的有希望的结果。

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