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Learning imprecise semantic concepts from image databases.

Daniel Sánchez, Jesús Chamorro-Martínez (2002)

Mathware and Soft Computing

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...

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