Peekaboo -物品在哪里？结构调整超像素
Peekaboo - Where are the Objects? Structure Adjusting Superpixels
摘要：
本文讨论了搜索一个快速和有意义的图像分割的背景下，$k$均值聚类。所提出的方法建立在广泛使用的本地版本的劳埃德算法，称为简单线性迭代聚类（SLIC）。我们提出了一种算法，扩展SLIC动态调整局部搜索，采用超像素分辨率动态结构存在于图像中，从而提供了更有意义的超像素在相同的线性运行时间作为标准SLIC。所提出的方法进行评估，对国家的最先进的技术和改进的边界粘附和欠分割错误进行观察，同时仍然是最快的算法进行测试。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
