Label number maximization in the slider model

Authors:
Dietmar Ebner;Gunnar W. Klau;René Weiskircher
Affiliations:
Institute of Computer Graphics and Algorithms, Vienna University of Technology;Institute of Computer Graphics and Algorithms, Vienna University of Technology;Institute of Computer Graphics and Algorithms, Vienna University of Technology
Venue:
GD'04 Proceedings of the 12th international conference on Graph Drawing
Year:
2004

Citing 5
Cited 3

An empirical study of algorithms for point-feature label placement

ACM Transactions on Graphics (TOG)
Drawing graphs nicely using simulated annealing

ACM Transactions on Graphics (TOG)
Point labeling with sliding labels

Computational Geometry: Theory and Applications - Special issue on applications and challenges
Practical extensions of point labeling in the slider model

Proceedings of the 7th ACM international symposium on Advances in geographic information systems
The HuGS platform: a toolkit for interactive optimization

Proceedings of the Working Conference on Advanced Visual Interfaces

Fast point-feature label placement for dynamic visualizations

Information Visualization
A chorem-based approach for visually analyzing spatial data

Journal of Visual Languages and Computing
Temporally coherent real-time labeling of dynamic scenes

Proceedings of the 3rd International Conference on Computing for Geospatial Research and Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

We consider the NP-hard label number maximization problem lnm: Given a set of rectangular labels, each of which belongs to a point feature in the plane, the task is to find a labeling for a largest subset of the labels. A labeling is a placement such that none of the labels overlap and each is placed so that its boundary touches the corresponding point feature. The purpose of this paper is twofold: We present a new force-based simulated annealing algorithm to heuristically solve the problem and we provide the results of a very thorough experimental comparison of the best known labeling methods on widely used benchmark sets. The design of our new method has been guided by the goal to produce labelings that are similar to the results of an experienced human performing the same task. So we are not only looking for a labeling where the number of labels placed is high but also where the distribution of the placed labels is good. Our experimental results show that the new algorithm outperforms the other methods in terms of quality while still being reasonably fast and confirm that the simulated annealing method is well-suited for map labeling problems.