Perception-based visual quality measures

Albuquerque, Georgia; Eisemann, Martin; Magnor, Marcus

doi:10.1109/vast.2011.6102437

Cited by 50 publications

(32 citation statements)

References 15 publications

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“…Albuquerque et al [25] have attempted to find a perception-based quality measure for scatter plots. First, users were asked to identify similarity between scatter plots, which was used to train a MDS embedding.…”

Section: Related Workmentioning

confidence: 99%

Perception-Based Evaluation of Projection Methods for Multidimensional Data Visualization

Etemadpour

Motta

Paiva

et al. 2015

IEEE Trans. Visual. Comput. Graphics

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Abstract-Similarity-based layouts generated by multidimensional projections or other dimension reduction techniques are commonly used to visualize high-dimensional data. Many projection techniques have been recently proposed addressing different objectives and application domains. Nonetheless, very little is known about the effectiveness of the generated layouts from a user's perspective, how distinct layouts from the same data compare regarding the typical visualization tasks they support, or how domain-specific issues affect the outcome of the techniques. Learning more about projection usage is an important step towards both consolidating their role in high-dimensional data analysis and taking informed decisions when choosing techniques. This work provides a contribution towards this goal. We describe the results of an investigation on the performance of layouts generated by projection techniques as perceived by their users. We conducted a controlled user study to test against the following hypotheses: (1) projection performance is task-dependent; (2) certain projections perform better on certain types of tasks; (3) projection performance depends on the nature of the data; and (4) subjects prefer projections with good segregation capability. We generated layouts of high-dimensional data with five techniques representative of different projection approaches. As application domains we investigated image and document data. We identified eight typical tasks, three of them related to segregation capability of the projection, three related to projection precision, and two related to incurred visual cluttering. Answers to questions were compared for correctness against 'ground truth' computed directly from the data. We also looked at subject confidence and task completion times. Statistical analysis of the collected data resulted in Hypotheses 1 and 3 being confirmed, Hypothesis 2 being confirmed partially and Hypotheses 4 could not be confirmed. We discuss our findings in comparison with some numerical measures of projection layout quality. Our results offer interesting insight on the use of projection layouts in data visualization tasks and provide a departing point for further systematic investigations.

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Section: Related Workmentioning

confidence: 99%

Perception-Based Evaluation of Projection Methods for Multidimensional Data Visualization

Etemadpour

Motta

Paiva

et al. 2015

IEEE Trans. Visual. Comput. Graphics

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“…The results have shown that the users can solve the task with the IGA: the visual and interactive loop can lead to better visualizations for the considered task. This result can be related for instance to [1], in which the human evaluation of scatter plots was also efficiently used. Again, this result is as expected according to our experience with IGA.…”

Section: Resultsmentioning

confidence: 94%

“…In such work, the visualization can be evaluated not with real users, but with a model of what they may possibly perceive. This model can be obtained either from user studies [1,13], existing theories and knowledge about visual perception [33,47]. With such a model, an evaluation function can be defined to evaluate how efficiently a user would perceive the visualization at hand.…”

Section: Automatic Evaluation and Generation Of Visualizationsmentioning

confidence: 99%

“…1 Finding a relevant method from the field of visual data mining (VDM), information visualization (IV) or visual analytics (VA) can be easy for an expert in visualization, but it can be more difficult for users who are less familiar with such methods. A recent paper [18] has shown that novice users had three main activities when performing a VDM/IV/VA task: selecting data attributes, selecting visualizations and finding mappings from data to visual attributes.…”

Section: Introductionmentioning

confidence: 99%

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VizAssist: an interactive user assistant for visual data mining

2015

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“…Their Rank By Feature tool helps a viewer to navigate through a relatively large corpus of statistical data. Other researchers have developed scagnostics-type measures for parallel coordinates [9], pixel displays [21], 3D scatterplots [11], and other graphics [23,2].…”

Section: Feature-based Approachesmentioning

confidence: 99%

ScagExplorer: Exploring Scatterplots by Their Scagnostics

Dang

Wilkinson

2014

2014 IEEE Pacific Visualization Symposium

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A scatterplot displays a relation between a pair of variables. Given a set of v variables, there are v(v − 1)/2 pairs of variables, and thus the same number of possible pairwise scatterplots. Therefore for even small sets of variables, the number of scatterplots can be large. Scatterplot matrices (SPLOMs) can easily run out of pixels when presenting high-dimensional data. We introduce a theoretical method and a testbed for assessing whether our method can be used to guide interactive exploration of high-dimensional data. The method is based on nine characterizations of the 2D distributions of orthogonal pairwise projections on a set of points in multidimensional Euclidean space. Working directly with these characterizations, we can locate anomalies for further analysis or search for similar distributions in a "large" SPLOM with more than a hundred dimensions. Our testbed, ScagExplorer, is developed in order to evaluate the feasibility of handling huge collections of scatterplots.

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Perception-based visual quality measures

Cited by 50 publications

References 15 publications

Perception-Based Evaluation of Projection Methods for Multidimensional Data Visualization

Perception-Based Evaluation of Projection Methods for Multidimensional Data Visualization

VizAssist: an interactive user assistant for visual data mining

ScagExplorer: Exploring Scatterplots by Their Scagnostics

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