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Peekaboo-Where are the Objects? Structure Adjusting Superpixels

机译:躲猫猫-对象在哪里?结构调整超像素

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This paper addresses the search for a fast and meaningful image segmentation in the context of k-means clustering. The proposed method builds on a widely-used local version of Lloyd's algorithm, called Simple Linear Iterative Clustering (SLIC). We propose an algorithm which extends SLIC to dynamically adjust the local search, adopting superpixel resolution dynamically to structure existent in the image, and thus provides for more meaningful superpixels in the same linear runtime as standard SLIC. The proposed method is evaluated against state-of-the-art techniques and improved boundary adherence and undersegmentation error are observed, whilst still remaining among the fastest algorithms which are tested.
机译:本文介绍了在k均值聚类的背景下寻求快速而有意义的图像分割的方法。所提出的方法建立在劳埃德(Lloyd)算法广泛使用的本地版本(称为简单线性迭代聚类(SLIC))的基础上。我们提出了一种算法,该算法扩展了SLIC以动态调整局部搜索,动态采用超像素分辨率来构造图像中存在的结构,从而在与标准SLIC相同的线性运行时间中提供了更有意义的超像素。针对最新技术对提出的方法进行了评估,观察到了改进的边界附着性和细分误差,同时仍然保留了经过测试的最快算法。

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