“Silhouette Image Recognition with Weighted Minimum Common Supergraph”

Research output: Contribution to journalArticlepeer-review


It is desired to recognize objects in images correctly. Recognition using structural feature has been studied. A single structure extracted by the medial axis transform from a digital silhouette image is not always an essential feature as a prototype that represents silhouettes in a category, because of noise and distortion. In this paper, we propose a method for recognizing silhouette images by obtaining an essential structure from the images of a category. The essential structure is defined as a weighted minimum common supergraph of graphs which are extracted from silhouette images. To show the validly of the proposed method, experiments are carried out for categorizing silhouette images.

Original languageEnglish
Pages (from-to)640-647
Number of pages8
JournalJournal of the Institute of Image Electronics Engineers of Japan
Issue number5
Publication statusPublished - 2009 Jan


  • medial axis transform
  • silhouette image
  • weighted minimum common supergraph

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Electrical and Electronic Engineering


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