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A Context-Based Region Labeling Approach for Semantic Image Segmentation

  • Thanos Athanasiadis
  • Phivos Mylonas
  • Yannis Avrithis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4306)

Abstract

In this paper we present a framework for simultaneous image segmentation and region labeling leading to automatic image annotation. The proposed framework operates at semantic level using possible semantic labels to make decisions on handling image regions instead of visual features used traditionally. In order to stress its independence of a specific image segmentation approach we applied our idea on two region growing algorithms, i.e. watershed and recursive shortest spanning tree. Additionally we exploit the notion of visual context by employing fuzzy algebra and ontological taxonomic knowledge representation, incorporating in this way global information and improving region interpretation. In this process, semantic region growing labeling results are being re-adjusted appropriately, utilizing contextual knowledge in the form of domain-specific semantic concepts and relations. The performance of the overall methodology is demonstrated on a real-life still image dataset from the popular domains of beach holidays and motorsports.

Keywords

Membership Degree Contextual Knowledge Relation Graph Visual Context Merge Region 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Thanos Athanasiadis
    • 1
  • Phivos Mylonas
    • 1
  • Yannis Avrithis
    • 1
  1. 1.School of Electrical and Computer EngineeringNational Technical University of AthensZographou, AthensGreece

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