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.
@article{urn:eudml:doc:39234,
title = {Learning imprecise semantic concepts from image databases.},
journal = {Mathware and Soft Computing},
volume = {9},
year = {2002},
pages = {59-73},
zbl = {1022.68045},
mrnumber = {MR1956875},
language = {en},
url = {http://dml.mathdoc.fr/item/urn:eudml:doc:39234}
}
Sánchez, Daniel; Chamorro-Martínez, Jesús. Learning imprecise semantic concepts from image databases.. Mathware and Soft Computing, Tome 9 (2002) pp. 59-73. http://gdmltest.u-ga.fr/item/urn:eudml:doc:39234/