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The
goal of image retrieval is to retrieve images "similar"
to a given query image by comparing the query and database using
visual attributes like color, texture and appearance. Here, we
discuss how to characterize appearance and use it for image retrieval.
We use two different approaches to image retrieval - using a local
and a global representation of appearance.
For
the local approach, visual appearance is represented by the outputs
of a set of Gaussian derivative filters applied to an image. These
outputs are computed offline and stored in a database. A query
is created by outlining portions of the query image deemed useful
for retrieval by the user (this may be changed interactively depending
on the results). The query is also filtered with Gaussian derivatives
and these outputs are compared with those from the database. The
images in the database are ranked on the basis of this comparison.
The technique has been experimentally tested on a database of
1600 images which includes a variety of images. The system does
not require prior segmentation of the database. Objects can be
embedded in arbitrary backgrounds. The system handles a range
of size variations and viewpoint variations up to 20 or 25 deg.
Global
representations of appearance may be compared by using histograms
of suitable features. We describe an approach which compares histograms
of curvature features. The main advantage of such global representations
is the speed of retrieval.
For
more information, contact Alice O'Dea at (301) 405-6444 or alice@cfar.umd.edu.
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