Geography Reference
In-Depth Information
5.2.1
Computing Salience
The first approach to the automatic identification of landmarks has been presented
by Raubal and Winter [ 35 ] . Their approach reflects the three landmark characteris-
tics of Sorrows and Hirtle [ 45 ] discussed in Chaps. 3 and 4 . Raubal and Winter's
approach aims at capturing perceptual and cognitive aspects of geographic objects,
which are then used to calculate landmark salience. Their formal model of landmark
salience is based on the concept of attractiveness , which reflects the 'landmarkness'
of an object, i.e., how strong a landmark candidate it is.
In accordance with Sorrows and Hirtle's conceptual classification of landmark
aspects, Raubal and Winter define three different kinds of attractiveness: visual ,
semantic ,and structural . Geographic objects are visually attractive if they are in
sharp contrast to their surroundings or have a prominent spatial location. The formal
model for landmark salience includes four measures of visual attractiveness:
￿
Façade area: If the façade area of an object is significantly larger or smaller
than those of the surrounding objects this object becomes well noticeable. In
the original model, façade area is simply measured as the product of width and
height, assuming rectangular buildings, however, this can be extended to account
for more irregular shapes as well.
￿
Shape: Unusual shapes, especially among more regular, box-like objects, are
highly remarkable. Indeed, architects use this to make buildings stand out. The
model distinguishes two aspects of shape, the shape factor and the deviation .
The shape factor simply is the proportion of height and width. The deviation is
the ratio of the area of the minimum-bounding rectangle (mbr) of the object's
façade and its façade area. Again, more complicated measures could be defined
if needed.
￿
Color: For humans, color is a clear indicator of visual attractiveness. For example,
a red building will be highly visible among a row of grey buildings. In the model,
color is measured as a decimal value derived from the RGB color space.
￿
Visibility: Like color, visibility is highly important for visual attractiveness. The
model assumes a two-dimensional visibility, defined by recognizability within
a buffer zone. Visibility is measured as the fraction (over its total size) of a
building's front (its façade) in this buffer zone.
While color seems to be an obvious measure for salience, in practice
challenges arise for automatically determining color differences in an image
because of the grey world assumption [ 3 ] . Essentially, this assumption states
that given enough color variation in an image, the average value of RGB will
be a common grey value. As a consequence, color variation may be far less
obvious for a computer algorithm than it is for the human eye.
 
 
 
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