By Topic

Similarity queries in image databases

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Santini, S. ; Dept. of Comput. Sci., California Univ., San Diego, La Jolla, CA, USA ; Jain, R.

Query-by-content image database will be based on similarity, rather than on matching, where 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 single complex query. We present a solution based on three operators that are the analogous of the and, or, and not operators one uses in traditional databases. These operators are powerful enough to express queries of unlimited complexity, yet have a very intuitive behavior, making easy for the user to specify a query tailored to a particular need

Published in:

Computer Vision and Pattern Recognition, 1996. Proceedings CVPR '96, 1996 IEEE Computer Society Conference on

Date of Conference:

18-20 Jun 1996