Learning imprecise semantic concepts from image databases

• Autores: Daniel Sánchez, Jesús Chamorro
• Localización: Mathware & soft computing: The Magazine of the European Society for Fuzzy Logic and Technology, ISSN-e 1134-5632, Vol. 9, Nº. 1, 2002, págs. 59-73
• Idioma: inglés
• Títulos paralelos:
  • Aprendizaje de conceptos semánticos imprecisos a partir de bases de datos de imágenes
• Enlaces
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• Resumen

  • In this paper we introduce a model to represent high-level semantic concepts that can be perceived in images. The concepts are learned and represented by means of a set of association rules that relate the presence of perceptual features to the fulfillment of a concept for a set of images. Since both the set of images where a perceptual feature appears and the set of images fulfilling a given concept are fuzzy, we use in fact fuzzy association rules for the learning model. The concepts so acquired are useful in several applications, in particular they provide a new way to formulate imprecise queries in image databases.