Visual Abstraction

Viola, Ivan; Chen, Min; Isenberg, Tobias

doi:10.1007/978-3-030-34444-3_2

Cited by 21 publications

(12 citation statements)

References 12 publications

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“…It survived qualitative falsification by using arguments in visualization [ 38 ]. It offered a theoretical explanation of “visual abstraction” [ 39 ]. It provided a theoretical basis to a design space that was structured according to different ways of “losing information” in origin-destination data visualization [ 40 ].…”

Section: Related Workmentioning

confidence: 99%

“…Consider the scenario of viewing some data through a particular visual representation. The term Alphabet Compression (AC) measures the amount of information loss due to visual abstraction [ 39 ] (or any transformation featuring many-to-one mappings). Since the visual representation is fixed in the scenario, AC is thus largely data-dependent.…”

Section: Overview and Motivationmentioning

confidence: 99%

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A Bounded Measure for Estimating the Benefit of Visualization (Part I): Theoretical Discourse and Conceptual Evaluation

Chen

Sbert

2022

Entropy

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Information theory can be used to analyze the cost–benefit of visualization processes. However, the current measure of benefit contains an unbounded term that is neither easy to estimate nor intuitive to interpret. In this work, we propose to revise the existing cost–benefit measure by replacing the unbounded term with a bounded one. We examine a number of bounded measures that include the Jenson–Shannon divergence, its square root, and a new divergence measure formulated as part of this work. We describe the rationale for proposing a new divergence measure. In the first part of this paper, we focus on the conceptual analysis of the mathematical properties of these candidate measures. We use visualization to support the multi-criteria comparison, narrowing the search down to several options with better mathematical properties. The theoretical discourse and conceptual evaluation in this part provides the basis for further data-driven evaluation based on synthetic and experimental case studies that are reported in the second part of this paper.

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Section: Related Workmentioning

confidence: 99%

Section: Overview and Motivationmentioning

confidence: 99%

A Bounded Measure for Estimating the Benefit of Visualization (Part I): Theoretical Discourse and Conceptual Evaluation

Chen

Sbert

2022

Entropy

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“…Hence, given a dataset, the best visualization is the one that loses the most information while causing the least distortion. This also explains why visual abstraction is effective when the viewers have adequate knowledge to reconstruct the lost information and may not be effective otherwise [ 63 ]. The central thesis by Chen and Golan [ 3 ] may appear to be counter-intuitive to many as it suggests “inaccuracy is a good thing”, partly because the word “inaccuracy” is an abstraction of many meanings and itself features information loss.…”

Section: Appendix A1 An Information-theoretic Measure For Cost-benefi...mentioning

confidence: 99%

Section: Appendix A1 An Information-theoretic Measure For Cost-benefi...mentioning

confidence: 99%

A Bounded Measure for Estimating the Benefit of Visualization (Part II): Case Studies and Empirical Evaluation

Chen

Abdul‐Rahman

Silver

et al. 2022

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Many visual representations, such as volume-rendered images and metro maps, feature a noticeable amount of information loss due to a variety of many-to-one mappings. At a glance, there seem to be numerous opportunities for viewers to misinterpret the data being visualized, hence, undermining the benefits of these visual representations. In practice, there is little doubt that these visual representations are useful. The recently-proposed information-theoretic measure for analyzing the cost–benefit ratio of visualization processes can explain such usefulness experienced in practice and postulate that the viewers’ knowledge can reduce the potential distortion (e.g., misinterpretation) due to information loss. This suggests that viewers’ knowledge can be estimated by comparing the potential distortion without any knowledge and the actual distortion with some knowledge. However, the existing cost–benefit measure consists of an unbounded divergence term, making the numerical measurements difficult to interpret. This is the second part of a two-part paper, which aims to improve the existing cost–benefit measure. Part I of the paper provided a theoretical discourse about the problem of unboundedness, reported a conceptual analysis of nine candidate divergence measures for resolving the problem, and eliminated three from further consideration. In this Part II, we describe two groups of case studies for evaluating the remaining six candidate measures empirically. In particular, we obtained instance data for (i) supporting the evaluation of the remaining candidate measures and (ii) demonstrating their applicability in practical scenarios for estimating the cost–benefit of visualization processes as well as the impact of human knowledge in the processes. The real world data about visualization provides practical evidence for evaluating the usability and intuitiveness of the candidate measures. The combination of the conceptual analysis in Part I and the empirical evaluation in this part allows us to select the most appropriate bounded divergence measure for improving the existing cost–benefit measure.

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“…Curve abstraction [EBB * 15] creates a visually abstracted [VI18,VCI20] representation of large DTI fiber sets without generating explicit fiber bundles. After finding the nearest neighbors within a given radius of a query point, the method iteratively contracts the fiber segments by drawing fiber samples closer to the similar ones.…”

Section: Curve Abstraction and Segmentationmentioning

confidence: 99%

Curve Complexity Heuristic KD‐trees for Neighborhood‐based Exploration of 3D Curves

Cheng

Isenberg

et al. 2021

Computer Graphics Forum

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We introduce the curve complexity heuristic (CCH), a KD-tree construction strategy for 3D curves, which enables interactive exploration of neighborhoods in dense and large line datasets. It can be applied to searches of k-nearest curves (KNC) as well as radius-nearest curves (RNC). The CCH KD-tree construction consists of two steps: (i) 3D curve decomposition that takes into account curve complexity and (ii) KD-tree construction, which involves a novel splitting and early termination strategy. The obtained KD-tree allows us to improve the speed of existing neighborhood search approaches by at least an order of magnitude (i. e., 28× for KNC and 12× for RNC with 98% accuracy) by considering local curve complexity. We validate this performance with a quantitative evaluation of the quality of search results and computation time. Also, we demonstrate the usefulness of our approach for supporting various applications such as interactive line queries, line opacity optimization, and line abstraction. CCS Concepts• Human-centered computing → Scientific visualization; • Theory of computation → Nearest neighbor algorithms;

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Visual Abstraction

Cited by 21 publications

References 12 publications

A Bounded Measure for Estimating the Benefit of Visualization (Part I): Theoretical Discourse and Conceptual Evaluation

A Bounded Measure for Estimating the Benefit of Visualization (Part I): Theoretical Discourse and Conceptual Evaluation

A Bounded Measure for Estimating the Benefit of Visualization (Part II): Case Studies and Empirical Evaluation

Curve Complexity Heuristic KD‐trees for Neighborhood‐based Exploration of 3D Curves

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