Label placement by maximum independent set in rectangles

Agarwal, Pankaj K.; Kreveld, Marc van; Suri, Subhash

doi:10.1016/s0925-7721(98)00028-5

Cited by 187 publications

(218 citation statements)

References 8 publications

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“…The recognized road labels, together with the identified road segments, are the input to this step. Map labeling is a well investigated technique in both cartography [Edmondson et al, 1996] and computer science [Agarwal et al, 1998;Doddi et al, 1997;Freeman, 2005]. In general, to label linear features in a map, a computer program or a cartographer places the labels in parallel to the corresponding linear features.…”

Section: Initial Association Of Road Labels and Road Segmentsmentioning

confidence: 99%

“…Figure 1 shows our overall approach, which integrates our previous map processing work (the interactive road vectorization [Chiang and Knoblock, 2011a] and text recognition techniques [Chiang, 2010;Chiang and Knoblock, 2011b]) and offers a new contribution: an automatic technique to identify individual road segments from the road vectorization results and then associate the recognized road labels with the road segments. This technique is the reverse engineering of cartographic-labeling methods [Agarwal et al, 1998;Doddi et al, 1997;Edmondson et al, 1996;Freeman, 2005]. The resulting named road vector data can be used in a geographic information system (GIS).…”

Section: Introductionmentioning

confidence: 99%

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Generating Named Road Vector Data from Raster Maps

Chiang

Knoblock

2012

Geographic Information Science

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Abstract. Raster maps contain rich road information, such as the topology and names of roads, but this information is "locked" in images and inaccessible in a geographic information system (GIS). Previous approaches for road extraction from raster maps typically handle this problem as raster-to-vector conversion and hence the extracted road vector data are line segments without the knowledge of road names and where a road starts and ends. This paper presents a technique that builds on the results from our previous road vectorization and text recognition work to generate named road vector data from raster maps. This technique first segments road vectorization results using road intersections to determine the lines that represent individual roads in the map. Then the technique exploits spatial relationships between roads and recognized text labels to generate road names for individual road segments. We implemented this approach in our map processing system, called Strabo, and demonstrate that the system generates accurate named road vector data on example maps with 92.83% accuracy.

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Section: Initial Association Of Road Labels and Road Segmentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Generating Named Road Vector Data from Raster Maps

Chiang

Knoblock

2012

Geographic Information Science

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“…Even for axis-parallel rectangular labels of arbitrary height and width, there is an approximation algorithm, however with a ratio of just 1/O(log n) [AvKS97]. If the label height (or width) is fixed though, the problem can be approximated by a factor of 1/2 in O(n log n) time.…”

Section: The Label Number Maximisation Problemmentioning

confidence: 99%

A Combinatorial Framework for Map Labeling

Wagner

Wolff

1998

Graph Drawing

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Abstract. The general map labeling problem consists in labeling a set of sites (points, lines, regions) given a set of candidates (rectangles, circles, ellipses, irregularly shaped labels) for each site. A map can be a classical cartographical map, a diagram, a graph or any other figure that needs to be labeled. A labeling is either a complete set of non-conflicting candidates, one per site, or a subset of maximum cardinality. Finding such a labeling is NP-hard. We present a combinatorial framework to attack the problem in its full generality. The key idea is to separate the geometric from the combinatorial part of the problem. The latter is captured by the conflict graph of the candidates and by rules which successively simplify this graph towards a near-optimal solution. We exemplify this framework at the problem of labeling point sets with axis-parallel rectangles as candidates, four per point. We do this such that it becomes clear how our concept can be applied to other cases. We study competing algorithms and do a thorough empirical comparison. The new algorithm we suggest is fast, simple and effective.

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“…Naturally, substantial research effort has been devoted into generalizing such algorithms to larger classes of graphs. Examples include algorithms proposed for circular arc graphs [13,15], disc graphs [11,16,19,20], rectangle graphs [1,5,9], multiple-interval graphs [4,8], and multiple-subtree graphs [14].…”

Section: Introductionmentioning

confidence: 99%

Optimization Problems in Dotted Interval Graphs

Hermelin

Mestre

Rawitz

2012

Graph-Theoretic Concepts in Computer Science

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The class of D-dotted interval (D-DI) graphs is the class of intersection graphs of arithmetic progressions with jump (common difference) at most D. We consider various classical graph-theoretic optimization problems when these are restricted to D-DI graphs of arbitrarily fixed D. We show that Maximum Independent Set, Minimum Vertex Cover, and Minimum Dominating Set can be solved in polynomial time in this graph class, answering an open question posed by Jiang [17]. We also show that Minimum Vertex Cover can be approximated within a factor of (1 + ε) for any ε > 0 in linear time. This algorithm generalizes to a wide class of deletion problems including the classical Minimum Feedback Vertex Set and Minimum Planar Deletion problems. Our algorithms are based on classical results in algorithmic graph theory and new structural properties of D-DI graphs that may be of independent interest.

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Label placement by maximum independent set in rectangles

Cited by 187 publications

References 8 publications

Generating Named Road Vector Data from Raster Maps

Generating Named Road Vector Data from Raster Maps

A Combinatorial Framework for Map Labeling

Optimization Problems in Dotted Interval Graphs

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