Pairwise similarity for line extraction from distorted images

Hideitsu Hino, Jun Fujiki, Shotaro Akaho, Yoshihiko Mochizuki, Noboru Murata

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish
Title of host publicationComputer Analysis of Images and Patterns - 15th International Conference, CAIP 2013, Proceedings
Number of pages8
EditionPART 2
Publication statusPublished - 2013
Event15th International Conference on Computer Analysis of Images and Patterns, CAIP 2013 - York, United Kingdom
Duration: 2013 Aug 272013 Aug 29

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume8048 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference15th International Conference on Computer Analysis of Images and Patterns, CAIP 2013
Country/TerritoryUnited Kingdom


  • distorted image
  • line detection
  • pairwise similarity
  • spectral clustering

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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