Efficient Unsupervised Image Segmentation by Optimum Cuts in Graphs

  • Hans H. C. Bejar
  • Lucy A. C. Mansilla
  • Paulo A. V. MirandaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11401)


In this work, a method based on optimum cuts in graphs is proposed for unsupervised image segmentation, that can be tailored to different objects, according to their boundary polarity, by extending the Oriented Image Foresting Transform (OIFT). The proposed method, named UOIFT, encompasses as a particular case the single-linkage algorithm by minimum spanning tree (MST), establishing important theoretical contributions, and gives superior segmentation results compared to other approaches commonly used in the literature, usually requiring a lower number of image partitions to isolate the desired regions of interest. The method is supported by new theoretical results involving the usage of non-monotonic-incremental cost functions in directed graphs. The results are demonstrated using a region adjacency graph of superpixels in medical and natural images.


Unsupervised segmentation Image Foresting Transform Graph-cut measure 



Thanks to CNPq (308985/2015-0, 486988/2013-9, FINEP1266/13), FAPESP (2014/12236-1, 2016/21591-5), NAP eScience and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) - Finance Code 001 for funding.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Hans H. C. Bejar
    • 1
  • Lucy A. C. Mansilla
    • 1
  • Paulo A. V. Miranda
    • 1
    Email author
  1. 1.Department of Computer Science, Institute of Mathematics and StatisticsUniversity of São PauloSão PauloBrazil

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