Extraction and Specialization of Geo-spatial Objects in Geo-images Using Semantic Compression Algorithm

Authors:
Giovanni Guzmán;Serguei Levachkine;Miguel Torres;Rolando Quintero;Marco Moreno
Affiliations:
Centre for Computer Research, National Polytechnical Institute, Mexico City, Mexico;Centre for Computer Research, National Polytechnical Institute, Mexico City, Mexico;Centre for Computer Research, National Polytechnical Institute, Mexico City, Mexico;Centre for Computer Research, National Polytechnical Institute, Mexico City, Mexico;Centre for Computer Research, National Polytechnical Institute, Mexico City, Mexico
Venue:
MICAI '08 Proceedings of the 7th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
Year:
2008

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Abstract

This paper describes an object oriented methodology for the semantic extraction of a geo-image, which is defined by a set of natural language labels. The approach is composed of two main stages: analysisand synthesis. The analysis stage detects the main geographic components of a geo-image by means of the color quantification, geometry and topology of the geospatial objects. The result of this stage is a set of geo-images with intensities that are approximately uniform. The synthesis stage extracts the main geographic objects that have been identified and a labeling process is made in two levels (general and specialized). The aim of the labeling process is to associate a label of the thematic to each region, taking into account the RGB characteristics of the geo-image. In order to specialize each geographic object, we have proposed a specialization algorithm that considers geometric and topologic relations among them, represented in geographic application domain ontology. As a result, the set of labels describes the semanticsof a geo-image.