Pairwise Similarity for Line Extraction from Distorted Images
Pairwise Similarity for Line Extraction from Distorted Images
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DOI：
10.1007/978-3-642-40246-3_31
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发表时间：
2013-08
期刊：
影响因子：
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通讯作者：
H. Hino;Jun Fujiki;S. Akaho;Yoshihiko Mochizuki;Noboru Murata
中科院分区：
文献类型：
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作者：
H. Hino;Jun Fujiki;S. Akaho;Yoshihiko Mochizuki;Noboru Murata
在包括图像处理在内的许多领域中，对给定数据集进行聚类是至关重要的。它在图像分割、目标检测等方面发挥着重要的作用。本文提出了一种为给定数据集建立相似度矩阵的框架，然后将该矩阵用于对数据集进行聚类。两点之间的相似性是基于其他点在连接这两点的线周围的分布情况来定义的。它可以捕捉两个点如何放置在同一条线上的程度。将相似度矩阵作为给定数据集的核矩阵，在此基础上进行谱聚类。使用人工设计的问题和真实世界中从失真图像中检测线条的问题的实验表明，使用所提出的相似性矩阵进行聚类具有良好的性能。
Clustering a given set of data is crucial in many fields including image processing. It plays important roles in image segmentation and object detection for example. This paper proposes a framework of building a similarity matrix for a given dataset, which is then used for clustering the dataset. The similarity between two points are defined based on how other points distribute around the line connecting the two points. It can capture the degree of how the two points are placed on the same line. The similarity matrix is considered as a kernel matrix of the given dataset, and based on it, the spectral clustering is performed. Clustering with the proposed similarity matrix is shown to perform well through experiments using an artificially designed problem and a real-world problem of detecting lines from a distorted image.