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A Region Thesaurus Approach for High-Level Concept Detection in the Natural Disaster Domain

  • Evaggelos Spyrou
  • Yannis Avrithis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4816)

Abstract

This paper presents an approach on high-level feature detection using a region thesaurus. MPEG-7 features are locally extracted from segmented regions and for a large set of images. A hierarchical clustering approach is applied and a relatively small number of region types is selected. This set of region types defines the region thesaurus. Using this thesaurus, low-level features are mapped to high-level concepts as model vectors. This representation is then used to train support vector machine-based feature detectors. As a next step, latent semantic analysis is applied on the model vectors, to further improve the analysis performance. High-level concepts detected derive from the natural disaster domain.

Keywords

Region Type Latent Semantic Analysis Model Vector Coarse Segmentation Hierarchical Cluster Approach 
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 2007

Authors and Affiliations

  • Evaggelos Spyrou
    • 1
  • Yannis Avrithis
    • 1
  1. 1.Image, Video and Multimedia Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou Str., 157 80 AthensGreece

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