1995
@techreport{Gra1995,
vgclass = {report},
vgproject = {cbir},
author = {Robert S. Gray},
title = {Content-based image retrieval: color and edges},
number = {PCS-TR95-252},
institution = {Dartmouth College, Computer Science},
address = {Hanover, NH},
month = {March},
year = {1995},
url = {ftp://ftp.cs.dartmouth.edu/TR/TR95-252.ps.Z},
abstract = {One of the tools that will be essential for future
electronic publishing is a powerful image retrieval system. The author
should be able to search an image database for images that convey the
desired information or mood; a reader should be able to search a corpus
of published work for images that are relevant to his or her needs.
Most commercial image retrieval systems associate keywords or text with
each image and require the user to enter a keyword or textual
description of the desired image. This text-based approach has
numerous drawbacks -- associating keywords or text with each image is a
tedious task; some image features may not be mentioned in the textual
description; some features are ``nearly impossible to describe with
text''; and some features can be described in widely different ways
[Niblack, 1993].
In an effort to overcome these problems and improve retrieval
performance, researchers have focused more and more on content-based
image retrieval in which retrieval is accomplished by comparing image
features directly rather than textual descriptions of the image
features. Features that are commonly used in content-based retrieval
include color, shape, texture and edges. In this report we describe a
simple content-based system that retrieves color images on the basis of
their color distributions and edge characteristics. The system uses two
retrieval techniques that have been described in the literature -- i.e.
histogram intersection to compare color distributions and sketch
comparison to compare edge characteristics. The performance of the
system is evaluated and various extensions to the existing techniques
are proposed.},
}