Revistes Catalanes amb Accés Obert (RACO)

Learning imprecise semantic concepts from image databases

Daniel Sánchez Fernández, Jesús Chamorro Martínez


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,
particularly because of user's subjectivity,
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.
An additional feature of our methodology is that it can capture user's subjectivity.

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