2000
@article{CNT2000,
vgclass = {refpap},
author = {Maurizio Cibelli and Michele Nappi and Maurizio Tucci},
title = {Content-based Access in Image Database by Quantitative
Relationships},
journal = {Journal of Visual Languages \& Computing},
volume = {11},
number = {5},
pages = {573--589},
month = {October},
year = {2000},
url = {http://dx.doi.org/10.1006/jvlc.2000.0171},
abstract = {In this paper, we describe a novel technique to perform
content-based access in image databases using quantitative spatial
relationships. Usually, spatial relation-based indexing methods fail if
the metric spatial information contained in the images must be
preserved. In order to provide a more robust approach to directional
relations indexing with respect to metric differences in images, this
paper introduces an improvement of the virtual image index, namely
quantitative virtual image, using a quantitative methodology. A scalar
quantitative measure is associated with each spatial relation, in order
to discriminate among images of the image database having the same
objects and spatial relationships, but different degree of similarity
if we also consider distance relationships. The measure we introduce
does not correspond to any significant increase of complexity with
respect to the standard virtual image providing a more precise answer
set.},
}