ImageScape query built with icons.
Department of Computer Science, Leiden University, The Netherlands.
A demo of the system is available at
Using the sketch interface, the user can draw an outline of the desired image.
For semantic querying, the user brings icons on a canvas that represent the
objects/concepts he is looking for, at the desired position in the image.
Examples of object/concept categories include human faces, stone or sand,
water, sky, tree or grass, points and lines (see figure 13).
Edge maps of the images collected by Web crawlers are obtained using the Sobel
operator and a Gaussian blurring filter.
A frequency histogram of the
binary pixel patterns occurring in the
edge image, which is called trigram vector, is computed for all images.
This vector is subjected to a dimensionality reduction using a band-pass filter
(see LCPDLCPD below).
Various other features, used in object matching, are taken at pixel level:
color, Laplacian, gradient magnitude, local binary patterns, invariant moments
and Fourier descriptors.
The first step of the object matching process uses the L1 distance on the
trigram vectors to retrieve the top
matches from the entire database.
Among these, 20 matches are selected in a second step, a
matching, using the most informative pixels to minimize the misdetection rate.
These pixels are found as follows.
For each object, a large set of positive and negative examples are used in
finding the set of 256 pixels with the greatest discriminatory power, by
maximazing the Kullback relative information combined with a Markov random