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Colour saliency-based parameter optimisation for adaptive colour segmentation

机译:基于颜色显着性的参数优化用于自适应颜色分割

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In this paper we present a parameter optimisation procedure that is designed to automatically initialise the number of clusters and the initial colour prototypes required by data space partitioning techniques. The proposed optimisation approach involves a colour saliency measure used in conjunction with a SOM classification procedure. For evaluation purposes, we have integrated the proposed initialisation technique in an unsupervised colour segmentation scheme based on K-Means clustering and the evaluation has been carried out in the context of the unsupervised segmentation of natural images.
机译:在本文中,我们提出了一种参数优化程序,该程序旨在自动初始化簇数和数据空间分区技术所需的初始颜色原型。所提出的优化方法涉及与SOM分类程序结合使用的颜色显着性度量。为了进行评估,我们将提出的初始化技术集成到了基于K-Means聚类的非监督颜色分割方案中,并且在自然图像的非监督分割的背景下进行了评估。

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