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Graph-Based Image Segmentation Using Dynamic Trees

  • Jordão BragantiniEmail author
  • Samuel Botter Martins
  • Cesar Castelo-Fernandez
  • Alexandre Xavier Falcão
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11401)

Abstract

Image segmentation methods have been actively investigated, being the graph-based approaches among the most popular for object delineation from seed nodes. In this context, one can design segmentation methods by distinct choices of the image graph and connectivity function—i.e., a function that measures how strongly connected are seed and node through a given path. The framework is known as Image Foresting Transform (IFT) and it can define by seed competition each object as one optimum-path forest rooted in its internal seeds. In this work, we extend the general IFT algorithm to extract object information as the trees evolve from the seed set and use that information to estimate arc weights, positively affecting the connectivity function, during segmentation. The new framework is named Dynamic IFT (DynIFT) and it can make object delineation more effective by exploiting color, texture, and shape information from those dynamic trees. In comparison with other graph-based approaches from the state-of-the-art, the experimental results on natural images show that DynIFT-based object delineation methods can be significantly more accurate.

Keywords

Image Foresting Transform Multiple object delineation Graph Cut Image segmentation by seed competition 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Jordão Bragantini
    • 1
    Email author
  • Samuel Botter Martins
    • 1
  • Cesar Castelo-Fernandez
    • 1
  • Alexandre Xavier Falcão
    • 1
  1. 1.Laboratory of Image Data ScienceUniversity of CampinasCampinasBrazil

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