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Uploaded byGuillaume Touya
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Touya et al_issdq_presentation

The document discusses methods for assessing visual clutter in generalized maps using image-based evaluation techniques. It highlights the complexity of global evaluation and presents various clutter measures such as edge density and subband entropy before and after map generalization. The authors conclude that image-based clutter measures are beneficial for evaluating generalization and suggest future comparisons with other methods.

Science◦
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 outputInitial 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 1:250k 0 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