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Computer Science Division, University of California, Berkeley.
http://elib.cs.berkeley.edu/photos/blobworld/.
A demo of Blobworld is available at
http://elib.cs.berkeley.edu/photos/blobworld/start.html.
[CTB+99].
The features used for querying are the color, texture, location, and shape
of regions (blobs) and of the background.
The color is described by a histogram of 218 bins of the color coordinates in
Lab-space.
Texture is represented by mean contrast and anisotropy over the region, as
the 2D coordinate
.
Shape is represented by (approximate) area, eccentricity, and orientation.
The user first selects a category, which already limits the search space.
In an initial image, the user selects a region (blob), and indicates the
importance of the blob (`somewhat', `very').
Next, the user indicates the importance of the blob's color, texture, location,
and shape (`not', `somewhat', `very').
More than one regions can be used for querying.
To match two color histograms h1 and h2, the quadratic form distance is
used:
d(h1, h2)=(h1-h2)TA(h1-h2), where
A=(aij) is a symmetric
matrix of weights representing the similarity between color bins i and j.
The distance between two texture descriptors is the Euclidean distance between
their coordinates in representation space.
The distance between centroids is the Euclidean distance.
The distances are combined into a single final distance.
Rather than actually computing the distances between the full color histogram
vectors of length 218 as
d(h1, h2)=(h1-h2)TA(h1-h2), singular value
decomposition (SVD) is used to project the histogram vectors onto a
lower-dimensional subspace.
The resulting points are indexed by an R*-tree [BKSS90].
The retrieved images are ranked in linear order, and presented together with
the segmented version showing the regions, see figure 4.
The demo on the web provides retrieval from a collection of 10000 Corel stock
photos.
Figure 4:
Blobworld query result.
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Next: CANDID
Up: Systems
Previous: BDLP
Remco Veltkamp
2001-03-08