Hierarchical Image Segmentation Relying on a Likelihood Ratio Test

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9280)


Hierarchical image segmentation provides a set of image segmentations at different detail levels in which coarser details levels can be produced by simple merges of regions from segmentations at finer detail levels. However, many image segmentation algorithms relying on similarity measures lead to no hierarchy. One of interesting similarity measures is a likelihood ratio, in which each region is modelled by a Gaussian distribution to approximate the cue distributions. In this work, we propose a hierarchical graph-based image segmentation inspired by this likelihood ratio test. Furthermore, we study how the inclusion of hierarchical property have influenced the computation of quality measures in the original method. Quantitative and qualitative assessments of the method on three well known image databases show efficiency.


Hierarchical image segmentation Graph-based method Statistical properties 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.PUC Minas - ICEI - DCC - VIPLABBelo HorizonteBrazil
  2. 2.Université Paris-Est, LIGM, ESIEE Paris - CNRSChamps-sur-MarneFrance

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