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