Fast Superpixel-Based hierarchical approach to image segmentation

Francesco Verdoja*, Marco Grangetto

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

3 Citations (Scopus)


Image segmentation is one of the core task in image processing. Traditionally such operation is performed starting from single pixels requiring a significant amount of computations. It has been shown that superpixels can be used to improve segmentation performance. In this work we propose a novel superpixel-based hierarchical approach for image segmentation that works by iteratively merging nodes of a weighted undirected graph initialized with the superpixels regions. Proper metrics to drive the regions merging are proposed and experimentally validated using the standard Berkeley Dataset. Our analysis shows that the proposed algorithm runs faster than state of the art techniques while providing accurate segmentation results both in terms of visual and objective metrics.

Original languageEnglish
Title of host publicationImage Analysis and Processing – ICIAP 2015 - 18th International Conference, Proceedings
Number of pages11
Publication statusPublished - 2015
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Image Analysis and Processing - Genoa, Italy
Duration: 7 Sep 201511 Sep 2015
Conference number: 18

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)03029743
ISSN (Electronic)16113349


ConferenceInternational Conference on Image Analysis and Processing
Abbreviated titleICIAP


  • Bhattacharyya distance
  • CIEDE2000
  • Graph partitioning
  • Hierarchical clustering
  • Mahalanobis distance
  • Segmentation
  • Superpixels


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