Shape classification using complex network and Multi-scale Fractal Dimension

  • Authors:
  • André Ricardo Backes;Odemir Martinez Bruno

  • Affiliations:
  • Universidade de São Paulo, Instituto de Ciências Matemáticas e de Computação, Av. do Trabalhador Sãocarlense, 400 13560-970 São Carlos, São Paulo, Brazil;Universidade de São Paulo, Instituto de Física de São Carlos, Av. do Trabalhador Sãocarlense, 400 13560-970 São Carlos, São Paulo, Brazil

  • Venue:
  • Pattern Recognition Letters
  • Year:
  • 2010

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Abstract

Shape provides one of the most relevant information about an object. This makes shape one of the most important visual attributes used to characterize objects. This paper introduces a novel approach for shape characterization, which combines modeling shape into a complex network and the analysis of its complexity in a dynamic evolution context. Descriptors computed through this approach show to be efficient in shape characterization, incorporating many characteristics, such as scale and rotation invariant. Experiments using two different shape databases (an artificial shapes database and a leaf shape database) are presented in order to evaluate the method, and its results are compared to traditional shape analysis methods found in literature.