Geostatistics for context-aware image classification

Felipe Codevilla, Silvia S.C. Botelho, Nelson Duarte, Samuel Purkis, A. S.M. Shihavuddin, Rafael Garcia, Nuno Gracias

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

7 Scopus citations


Context information is fundamental for image understanding. Many algorithms add context information by including semantic relations among objects such as neighboring tendencies, relative sizes and positions. To achieve context inclusion, popular context-aware classification methods rely on probabilistic graphical models such as Markov Random Fields (MRF) or Conditional Random Fields (CRF). However, recent studies showed that MRF/CRF approaches do not perform better than a simple smoothing on the labeling results. The need for more context awareness has motivated the use of different methods where the semantic relations between objects are further enforced. With this, we found that on particular application scenarios where some specific assumptions can be made, the use of context relationships is greatly more effective. We propose a new method, called GeoSim, to compute the labels of mosaic images with context label agreement. Our method trains a transition probability model to enforce properties such as class size and proportions. The method draws inspiration from Geostatistics, usually used to model spatial uncertainties. We tested the proposed method in two different ocean seabed classification context, obtaining state-of-art results.

Original languageEnglish (US)
Title of host publicationComputer Vision Systems - 10th International Conference, ICVS 2015, Proceedings
EditorsAntonios Gasteratos, Lazaros Nalpantidis, Volker Kruger, Jan-Olof Eklundh
PublisherSpringer Verlag
Number of pages12
ISBN (Print)9783319209036
StatePublished - 2015
Externally publishedYes
Event10th International Conference on Computer Vision Systems, ICVS 2015 - Copenhagen, Denmark
Duration: Jul 6 2015Jul 9 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other10th International Conference on Computer Vision Systems, ICVS 2015


  • Conditional random fields
  • Context adding
  • Geostatistics
  • Underwater vision

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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