Resumen de Learning imprecise semantic concepts from image databases
Daniel Sánchez, Jesús Chamorro 
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.
