Pairwise Similarity for Line Extraction from Distorted Images

  • Hideitsu Hino
  • Jun Fujiki
  • Shotaro Akaho
  • Yoshihiko Mochizuki
  • Noboru Murata
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8048)


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.


pairwise similarity spectral clustering line detection distorted image 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Hideitsu Hino
    • 1
  • Jun Fujiki
    • 2
  • Shotaro Akaho
    • 3
  • Yoshihiko Mochizuki
    • 4
  • Noboru Murata
    • 4
  1. 1.University of TsukubaTsukubaJapan
  2. 2.Fukuoka UniversityJonan-kuJapan
  3. 3.National Institute of Advanced Industrial Science and TechnologyTsukubaJapan
  4. 4.Waseda UniversityShinjuku-kuJapan

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