Touya et al_issdq_presentation
35
COMPARING IMAGE-BASED METHODS FOR ASSESSING VISUAL CLUTTER IN GENERALIZED MAPS G. Touya – B. Decherf – M. Lalanne – M. Dumont COGIT team IGN France ISSDQ 2015 – Geospatial week
Transcript of Touya et al_issdq_presentation
COMPARING IMAGE-BASED METHODS FOR ASSESSING VISUAL CLUTTER IN GENERALIZED MAPS
G. Touya – B. Decherf – M. Lalanne – M. Dumont
COGIT team IGN France
ISSDQ 2015 – Geospatial week
Evaluation of map generalization
Map output Initial
data
Select situation and
algorithms
Apply selected process
Accept or cancel
evaluation required
Evaluation of map generalization
• User map requirements
• Cartographic rules Generalization constraints
Evaluation of map generalization
• User map requirements
• Cartographic rules Generalization constraints
« Building area > 0.4 map mm² »
« Building granularity > 0.1 map mm »
« Building alignments should be preserved »
« Building/road distance > 0.1 map mm »
Evaluation of map generalization
0
20
40
60
80
1 2 3 4 5 6 7 8
Constraints number
satisfaction scale
Evaluation of map generalization
0
20
40
60
80
1 2 3 4 5 6 7 8
Constraints number
satisfaction scale
Global evaluation is complex!
Using image-based evaluation
[Rylov & Reimer 2014]
Clutter in computer vision
Excessive and/or disorganized information
Edge density clutter
Quad-tree based clutter
[Jégou & Deblonde 2012]
Quad-tree based clutter
[Jégou & Deblonde 2012]
Subband entropy clutter
Similar to jpeg compression
[Rosenholtz et al 2007]
Subband entropy clutter
[Jégou & Deblonde 2012]
Segmentation based clutter
[Bravo & Farid 2008]
Tested maps
Initial data 1:15k symbols
Initial data 1:50k symbols
Generalized data 1:50k symbols
Tested maps
Before and after, manually generalized
Tested maps
Before and after, automatically generalized
[Touya & Duchêne 2011]
Experiments
Identifying Too Cluttered Areas Initial map edges
quad tree segmentation
Preserving the global amount of
information
1:25k 1:100k
1:250k 1:1000k
Edge density Subband entropy Quad tree Segmentation 4000
3000
2000
1000
0
2000
1500
1000
500
0
10.5 10
9.5 9
8.5 8
0.15
0.1
0.05
0
Preserving the global amount of
information
1:25k 1:100k
1:250k 1:1000k
Edge density Subband entropy Quad tree Segmentation 6000
4000
2000
0
1500
1000
500
0
15
10
5
0
0.2 0.15
0.1 0.05
0
Preserving the global amount of
information
0
500
1000
1500
2000
1:25k 1:100k 1:250k
0
5
10
15
1:25k 1:100k 1:250k0
0,02
0,04
0,06
0,08
0,1
1:25k 1:100k 1:250k
0
1000
2000
3000
4000
1:25k 1:100k 1:250k
Edge density Subband entropy Quad tree Segmentation
1:25k 1:100k 1:250k
Handling of occlusions and overlaps
Clutter measure Before generalization After generalization
Edge density 6664 9268
Subband entropy 4.1 4.39
Quad tree 0.042 0.062
Segmentation 398 389
Handling of occlusions and overlaps
Clutter measure Before generalization After generalization
Edge density 6664 9268
Subband entropy 4.1 4.39
Quad tree 0.042 0.062
Segmentation 398 389
Handling of occlusions and overlaps
Initial map Generalized map
Background and foreground
Edge density Subband entropy
Quad tree Segmentation
initial map 1432 4.91 0.058 1076
no background 2427 2.39 0.028 431
transparency 1543 4.17 0.028 633
paler shades 542 4.95 0.028 927
Background and foreground
Edge density Subband entropy
Quad tree Segmentation
initial map 1432 4.91 0.058 1076
no background 2427 2.39 0.028 431
transparency 1543 4.17 0.028 633
paler shades 542 4.95 0.028 927
Background and foreground
Edge density Subband entropy
Quad tree Segmentation
initial map 1432 4.91 0.058 1076
no background 2427 2.39 0.028 431
transparency 1543 4.17 0.028 633
paler shades 542 4.95 0.028 927
Background and foreground Including
contour lines
Excluding contour lines
Legible cell
Cluttered cell
[Olsson et al 2011]
Reduction of blank space
Clutter measure Before generalization
After generalization
Edge density 429 1459
Subband entropy 1.73 1.75
Quad tree 0.032 0.035
Segmentation 1630 1765
Reduction of blank space
Clutter measure Before generalization
After generalization
Edge density 429 1459
Subband entropy 1.73 1.75
Quad tree 0.032 0.035
Segmentation 1630 1765
Conclusion
• Image-based clutter measures can be useful for
generalization evaluation
• Clutter measures vary differently with
generalization
What’s next?
• Test other methods (feature congestion, color
clustering, crowding model…)
• Compare with vector-based methods
• Combine several complementary methods
• Compare to generalization evaluation methods
Any Question?
G. Touya – B. Decherf – M. Lalanne – M. Dumont
COGIT team IGN France
ISSDQ 2015 – Geospatial week
Implementation detail
• All is available in open source Java platform
GeOxygene
• Use of OpenIMAJ library [Hare et al 2011]
• Test images can be made available on demand for
comparisons
