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Learning imprecise semantic concepts from image databases.

Daniel SánchezJesús 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...

Segmenting colour images on the basis of a fuzzy hierarchical approach.

In this paper we deal with two problems related to imprecision in colour image segmentation processes: to decide whether a set of pixels verify the property to be homogeneously coloured, and to represent the set of possible segmentations of an image at different precision levels. In order to solve the first problem we introduce a measure of distance between colours in the CIE L*a*b* space, that allows us to measure the degree of homogeneity of two pixels p and q on the basis of the maximum distance...

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