Data Visualization Optimization via Computational Modeling of Perception

Abstract-Mapping a set of categorical values to different colors is an elementary technique in data visualization. Users of visualization software routinely rely on the default colormaps provided by a system, or colormaps suggested by software such as ColorBrewer. In practice, users often have to select a set of colors in a semantically meaningful way (e.g., based on conventions, color metaphors, and logological associations), and consequently would like to ensure their perceptual differentiation is optimized. In this paper, we present an algorithmic approach for maximizing the perceptual distances among a set of given colors. We address two technical problems in optimization, i.e., (i) the phenomena of local maxima that halt the optimization too soon, and (ii) the arbitrary reassignment of colors that leads to the loss of the original semantic association. We paid particular attention to different types of constraints that users may wish to impose during the optimization process. To demonstrate the effectiveness of this work, we tested this technique in two case studies. To reach out to a wider range of users, we also developed a web application called Colourmap Hospital.

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“…Global optimization techniques have been used previously in the field of visualization (e.g., [36,29]). Fig.…”

Section: General Optimizationmentioning

confidence: 99%

Categorical Colormap Optimization with Visualization Case Studies

Fang

Walton

Delahaye³

et al. 2017

IEEE Trans. Visual. Comput. Graphics

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“…It is becoming increasingly recognized that the properties of human [1] perception play a vital role in determining the effectiveness of data visualizations. For example, the most successful flow visualizations contain contours tangential to the flow field.…”

Section: Issn: 2319 -1058mentioning

confidence: 99%

“…This method is applied to the evaluation and optimization of two visualization types 2D flow visualizations and node-link graph visualizations. The computational perceptual model is applied to various visual representations of flow fields evaluated using the advection task of Laidlaw et al [1]. The predictive power of the model is examined by comparing its performance to that of human subjects on the advection task using four flow visualization types.…”

mentioning

confidence: 99%

M3: Model data representation using virtual reality [Model Qualify, Model Verify, Model Validity]

2017

IJIET

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Abstract-Researchers have argued that perceptual issues are important in determining what makes an effective visualization, but generally only provide descriptive guidelines for transforming perceptual theory into practical designs.In order to bridge the gap between theory and practice in a more rigorous way, a computational model of the primary visual cortex is used to explore the perception of data visualizations. A method is presented for automatically evaluating and optimizing data visualizations for an analytical task using a computational model of human vision. The method relies on a neural network simulation of early perceptual processing in the retina and visual cortex. The neural activity resulting from viewing information visualization is simulated and evaluated to produce metrics of visualization effectiveness for analytical tasks. Visualization optimization is achieved by applying these effectiveness metrics as the utility function in a hill-climbing algorithm. This method is applied to the evaluation and optimization of two visualization types 2D flow visualizations and node-link graph visualizations. The computational perceptual model is applied to various visual representations of flow fields evaluated using the advection task of Laidlaw et al. [1]. The predictive power of the model is examined by comparing its performance to that of human subjects on the advection task using four flow visualization types. The results show the same overall pattern for humans and the model. In both cases, the best performance was obtained from visualizations containing aligned visual edges. Flow visualization optimization is done using both streaklet-based and pixel-based visualization parameterizations. An emergent property of the streaklet-based optimization is head-to-tail streaklet alignment, the pixel-based parameterization results in a LIC-like result. Here we propose M3 Visualization for mathematical approach for improve HCI performance in statistical representation.

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“…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%