Measuring Aesthetics for Information Visualization

Filonik, Daniel; Baur, Dominikus

doi:10.1109/iv.2009.94

Cited by 28 publications

(20 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By contrast, the structure of networks is solely dependent on a set of vertices and the edges joining them [50]. Attaching to a graph some geometrical and visual features [51] such as edge crossings and edge bends gives rise to the kind of measures developed by [13]. The stylistic properties we intend to capture here are restricted to the structural properties of graphs.…”

Section: Methods and Discussionmentioning

confidence: 99%

Toward Measuring Network Aesthetics Based on Symmetry

Chen

Dehmer

Emmert‐Streib

et al. 2017

Axioms

View full text Add to dashboard Cite

Abstract:In this exploratory paper, we discuss quantitative graph-theoretical measures of network aesthetics. Related work in this area has typically focused on geometrical features (e.g., line crossings or edge bendiness) of drawings or visual representations of graphs which purportedly affect an observer's perception. Here we take a very different approach, abandoning reliance on geometrical properties, and apply information-theoretic measures to abstract graphs and networks directly (rather than to their visual representaions) as a means of capturing classical appreciation of structural symmetry. Examples are used solely to motivate the approach to measurement, and to elucidate our symmetry-based mathematical theory of network aesthetics.

show abstract

Section: Methods and Discussionmentioning

confidence: 99%

Toward Measuring Network Aesthetics Based on Symmetry

Chen

Dehmer

Emmert‐Streib

et al. 2017

Axioms

View full text Add to dashboard Cite

show abstract

“…It combined the interrelation between the user's mental state (expectations, needs and mood), attributes of the technology (complexity, functionality, and usability), and the setting in which the technology is being used (social gathering, in the workplace, everyday use). In conclusion, aesthetics is a contributing factor for successful communication (Filonik & Baur, 2009). …”

Section: Hci In 3d Avatar Designmentioning

confidence: 94%

“…As noted above, the main term that is associated with aesthetics is beauty; many people think that if something is aesthetically attractive, it is therefore beautiful (Filonik & Baur, 2009). Beauty, according to Tractinsky (2004), may be conceptualized as the quality of use of an object and virtuosity.…”

Section: The Role Of Aesthetics In 3d Avatar Designmentioning

confidence: 99%

The Influences of User Experience, Aesthetics and Psychology in the Design Process of 3D Avatars (Theoretical model)

Photiadis

Souleles

2015

JVWR

View full text Add to dashboard Cite

This paper introduces a theoretical model that combines three theoretical factors which -it is argued -significantly contribute to the operation of designing 3D avatars. These factors are aesthetics, user-experience and psychology. The aim of the report is to put forward new ideas on what informs the design operation of 3D avatars. An additional reason for the creation of this theoretical model is to simplify the procedure of 3D avatars design, while at the same time comprehending the influence of aesthetics, user-experience and psychology. The paper provides an overview of existing research on aesthetics, user experience and psychology and how these can inform 3D avatars design procedure. As with all theoretical models, this one too needs further testing; a set of future research questions are posed.

show abstract

“…Van Wijk [19] proposed an economic benefits and costs model to compute the value of a visualization. In contrast, Filonik and Baur [20] considered the aesthetics of a visualization as a quality metric. Recently, Jänicke and Chen [21] proposed a salience-based metric to evaluate visualizations in general, but also used flow visualizations including a LIC image as a specific example.…”

Section: Previous Workmentioning

confidence: 99%

A Metric for the Evaluation of Dense Vector Field Visualizations

Matvienko

Krüger

2013

IEEE Trans. Visual. Comput. Graphics

View full text Add to dashboard Cite

In this work, we present an intuitive image-quality metric that is derived from the motivation of DVF visualization. It utilizes the features of the resulting image and effectively measures the similarity between the output of the visualization method and the input flow data. We use the angle between the gradient direction and the original vector field as a measure of such similarity and the gradient magnitude as an importance measure. Our metric enables the automatic evaluation of images for a given vector field and allows the comparison of different methods, parameters sets, and quality improvement strategies for a specific vector field. By integrating the metric into the image-computation process, our approach can be used to generate improved images by choosing the best parameter set. To verify the effectiveness of our method, we conducted an extensive user study that demonstrated the metric’s applicability to various situations. For instance, our approach elucidated the robustness of a DVF visualization in the presence of data-altering filters, such as resampling.

show abstract

Measuring Aesthetics for Information Visualization

Cited by 28 publications

References 16 publications

Toward Measuring Network Aesthetics Based on Symmetry

Toward Measuring Network Aesthetics Based on Symmetry

The Influences of User Experience, Aesthetics and Psychology in the Design Process of 3D Avatars (Theoretical model)

A Metric for the Evaluation of Dense Vector Field Visualizations

Contact Info

Product

Resources

About