Many-to-One Boundary Labeling

Lin, Chun-Cheng; Kao, Hao Jen; Yen, Hsu Chun

doi:10.7155/jgaa.00169

Cited by 21 publications

(19 citation statements)

References 11 publications

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“…Finally, two papers considered many‐to‐one boundary labeling, where sets of point features can share the same label. Lin et al [LKY08] considered individual po‐leaders and labels with multiple ports, one for each feature with that label. They proved that minimizing crossings in 1‐sided many‐to‐one boundary labeling with po‐leaders is NP‐hard, but they also presented a greedy heuristic iteratively assigning label positions with locally fewest leader crossings.…”

Section: State Of the Artmentioning

confidence: 99%

“…Many‐to‐one labeling has also been studied for the 2‐sided setting with labels on opposite sides. Since the crossing minimization problem considered by Lin et al [LKY08] remains NP‐hard for the 2‐sided case, they again described a heuristic algorithm to obtain a labeling, in which all features sharing the same label connect with their own po‐leader to a single label box on one of the two opposite boundaries of C A . Their algorithm constructs a weighted graph, in which they (heuristically) determine a vertex bisection of minimum weight, which induces the partition of labels to the two boundary sides.…”

Section: State Of the Artmentioning

confidence: 99%

“…1‐sided boundary labelings with opo‐leaders have also been studied in the many‐to‐one setting, in which a point can be attached to more than one label [LKY08, Lin10]. Lin et al [LKY08] observed that in this setting crossings will inevitably occur, and proved that minimizing their number is NP‐hard. Finally, they presented a 3‐approximation algorithm, which places the labels in “median order” as introduced by Eades and Wormald [EW94].…”

Section: State Of the Artmentioning

confidence: 99%

“…Publications: [HAS04], [BG05], [FLH∗06], [GHS06a], [LKY08], [SD08], [ČB10], [WTLY11], [MTSP12], [TKS13], [HGK14], [GHN15], [ČB18].…”

Section: Labeling Techniquesmentioning

confidence: 99%

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External Labeling Techniques: A Taxonomy and Survey

Bekos¹,

Niedermann

Nöllenburg

2019

Computer Graphics Forum

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External labeling is frequently used for annotating features in graphical displays and visualizations, such as technical illustrations, anatomical drawings, or maps, with textual information. Such a labeling connects features within an illustration by thin leader lines with their labels, which are placed in the empty space surrounding the image. Over the last twenty years, a large body of literature in diverse areas of computer science has been published that investigates many different aspects, models, and algorithms for automatically placing external labels for a given set of features. This state‐of‐the‐art report introduces a first unified taxonomy for categorizing the different results in the literature and then presents a comprehensive survey of the state of the art, a sketch of the most relevant algorithmic techniques for external labeling algorithms, as well as a list of open research challenges in this multidisciplinary research field.

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Section: State Of the Artmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

“…Publications: [HAS04], [BG05], [FLH∗06], [GHS06a], [LKY08], [SD08], [ČB10], [WTLY11], [MTSP12], [TKS13], [HGK14], [GHN15], [ČB18].…”

Section: Labeling Techniquesmentioning

confidence: 99%

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External Labeling Techniques: A Taxonomy and Survey

Bekos¹,

Niedermann

Nöllenburg

2019

Computer Graphics Forum

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“…This model was formally introduced by Lin et al [7], who assumed that each label has one port for each connecting feature point (see Fig. 1a) and showed that several crossing minimization problems are NP-complete and, subsequently, developed approximation and heuristic algorithms.…”

Section: Introductionmentioning

confidence: 99%

Many-to-One Boundary Labeling with Backbones

et al. 2013

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Abstract. In this paper we study many-to-one boundary labeling with backbone leaders. In this model, a horizontal backbone reaches out of each label into the feature-enclosing rectangle. Feature points associated with this label are linked via vertical line segments to the backbone. We present algorithms for label number and leader-length minimization. If crossings are allowed, we aim to minimize their number. This can be achieved efficiently in the case of fixed label order. We show that the corresponding problem in the case of flexible label order is NP-hard.

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