Parameter-free hierarchical image segmentation

S M Abdullah, Peter Tischer, Sudanthi Wijewickrema, Andrew Paplinski

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

1 Citation (Scopus)


Images typically have many levels of detail and the suitability of a segmentation depends on application requirements. Thus, it is imperative that the user/application be given the option to select the 'optimal' segmentation that captures the desired level of detail from a set of segmentations. This paper presents a hierarchical image segmentation algorithm that offers this option using the concept of minimum spanning trees. It converts an input image into a tree structure from which a hierarchy of segmentations is obtained through a process of merging. No parameters are used in this process and thus the proposed algorithm can be used on any segmentation dataset as is. The levels are calculated in one pass of the minimum spanning tree and as such, no iterative merging is required. This provides the user with a quick way of segment visualisation. Evaluation results on two popular segmentation datasets show that the algorithm provides competitive results in comparison to other segmentation algorithms.

Original languageEnglish
Title of host publication2017 IEEE Visual Communications and Image Processing (VCIP)
EditorsEckehard Steinbach, Weisi Lin, Carlos Busso, Siwei Ma
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages4
ISBN (Electronic)9781538604625
ISBN (Print)9781538604632
Publication statusPublished - 2017
EventIEEE Visual Communications and Image Processing 2017 - St Petersburg, United States of America
Duration: 10 Dec 201713 Dec 2017 (Proceedings)


ConferenceIEEE Visual Communications and Image Processing 2017
Abbreviated titleVCIP 2017
Country/TerritoryUnited States of America
CitySt Petersburg
OtherIEEE Visual Communications and Image Processing
Internet address


  • hierarchical
  • MST
  • parameter-free
  • segment visualiser
  • segmentation

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