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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.
Bibliographic citation for this report: [plain text] [BIB] [BibTeX] [Refer]
Or copy and paste:
Robert S. Gray, "Content-based image retrieval: color and edges ." Dartmouth Computer Science Technical Report PCS-TR95-252, March 1995.
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