1996
@inproceedings{SaJ1996,
vgclass = {refpap},
vgproject = {cbir},
author = {Simone Santini and Ramesh Jain},
title = {Similarity Queries in Image Databases},
booktitle = {IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
address = {San Francisco, California},
pages = {646--651},
month = {June},
year = {1996},
abstract = {Query-by-content image database will be based on
similarity, rather than matching, where \emph{similarity} is a measure
that is defined and meaningful for every pair of images in the image
space. Since it is the human user that, in the end, has to be
satisfied with the results of the query, it is natural to base the
similarity measure that we will use on the characteristics of human
similarity assessment. In the first part of this paper, we review some
of these characteristics and define a similarity measure based on them.
Another problem that similarity-based databases will have to face is
how to combine different queries into a similar complex query. We
present a solution based on three operators that are analogous of the
\emph{and}, \emph{or}, and \emph{not} operators one uses in traditional
databases. These operators are powerful enough to express queries of
unlimited complexity, yet have a very intuitive behaviour, making easy
for the user to specify a query tailored to a particular need.},
}