Geostatistics for Context-Aware Image Classification

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

DOI: 10.1007/978-3-319-20904-3_22

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9163)
Cite this paper as:
Codevilla F. et al. (2015) Geostatistics for Context-Aware Image Classification. In: Nalpantidis L., Krüger V., Eklundh JO., Gasteratos A. (eds) Computer Vision Systems. ICVS 2015. Lecture Notes in Computer Science, vol 9163. Springer, Cham

Abstract

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.

Keywords

Context adding Underwater vision Geostatistics Conditional random fields 

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Felipe Codevilla
    • 1
  • Silvia S. C. Botelho
    • 1
  • Nelson Duarte
    • 1
  • Samuel Purkis
    • 2
  • A. S. M. Shihavuddin
    • 3
  • Rafael Garcia
    • 3
  • Nuno Gracias
    • 3
  1. 1.Center of Computational Sciences (C3)Federal University of Rio Grande (FURG)Rio GrandeBrazil
  2. 2.National Coral Reef InstituteNova Southeastern UniversityDania BeachUSA
  3. 3.Computer Vision and Robotics Institute, Centre d’Investigació En Robòtica SubmarinaUniversitat de GironaGironaSpain

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