In: Wagner W., SzSkely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B 
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should be based upon a reasonable compromise between 
computational power and representativeness of the sketch. 
In particular, for 220x110-pattems a 9x9-window gives already 
acceptable results. It appeared to be possible to increase the 
size of the window even to 13x13 (Table 2). 
Table 2. Sketches for proximity windows of different sizes. 
In addition, one significant thing was revealed, i.e. spatial 
position and quantitative composition of raster elements 
essential for visual representation of a homogeneous region 
remain the same even at a big change of visual detail(Table 3). 
Samples 
manufactur 
ed forest 
Sketches 
Samples 
deciduous 
forest 
Sketches 
rare forest 
with sand 
■ 
glade with 
trees 
resident 
sites 
dachas 
Samples of 
pine forest 
Satellite, 
resolution, 
band 
Sketches 
QuickBird 
0,7 M 
0,445-0,90 
Table 3. Pine forest samples and sketches for different 
resolutions. 
It turns out that the property to seize visual information, if 
patterns are similar in more than one criterion, is sufficient for 
making comparison of natural objects: 
1. Referred to the same class and received with different 
resolution (Table 3). 
2. Referred to different classes and received with the 
same resolution (Table 4). 
Table 4. Quickbird-samples and corresponding sketches. 
3. CONCEPTUAL QUERY IN IMAGE DATA BASE 
The sketch (1) of textural pattern is the characteristic of the 
pattern that allows us to discern different textures. The measure 
of visual dissimilarity can be used for the arrangement of all 
images in image data base (IDB) in order of increasing 
similarity with the query pattern as follows: 
1. Enter the query spatial-homogenous pattern s. 
2. Retrieve the pattern sketch in accord with the model 
(1). 
3. Calculate measures of dissimilarity Dist(s„s) between 
the query pattern and images [s,: t=l,2,...] from IDB. 
4. Choose image s t * with the least value of dissimilarity 
measure as the first retrieval result: 
Distfs^ ,s) = min\Dist(s,, s) 1 
t S ( eIDB' t • 
Choose the subsequent retrieval results by ranking the values 
Dist{s„s) in increasing order. 
The experiments were carried out with two IDBs: 
1. IDB-1 that contains the patterns of Brodatz textures 
(Brodatz, 1966). The pattern classification for 32 
classes was taken as a basis for visual comparison of 
results of IDB-1 queries (Ma, 1996). The 
classification of various groups of experts can be 
different; therefore, the experiment was followed by 
certain changes in the set of classes as a result of the 
self-training of retrieval system. 
2. IDB-2 that contains 64 patterns of high resolution 
image (Quickbird , 0.7m) and grouped together into 
12 classes of GUMs as follows: bushes, resident sites,