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Special Issue "Information Theory Application in Visualization"

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".

Deadline for manuscript submissions: 30 April 2019

Special Issue Editors

Guest Editor
Prof. Dr. Mateu Sbert

Department of Informàtica i Matemàtica Aplicada, University of Girona, 17071 Girona, Spain, and Computer Science, Tianjin University, Tianjin 300072, China
Website 1 | Website 2 | E-Mail
Interests: application of Monte Carlo; integral geometry and information theory techniques to radiosity; global illumination; visualization and image processing
Guest Editor
Prof. Dr. Min Chen

Pembroke College, University of Oxford, Oxford OX1 1DW, UK
Website 1 | Website 2 | E-Mail
Interests: visualization; computer graphics and human-computer interaction
Guest Editor
Prof. Dr. Han-Wei Shen

Computer Science and Engineering, The Ohio State University, Columbus, OH 43210, USA
Website 1 | Website 2 | E-Mail
Interests: scientific visualization; computer graphics

Special Issue Information

Dear Colleagues,    

Information theory is “the science of quantification, coding and communication of information” (Usher, 1984). Since the pioneering work by Shannon and Wiener in the late 1940s, information theory has played an underpinning role in the field of tele- and data communication. It has also been applied to disciplines such as physics, biology, neurology, and psychology. In computer science, its applications include computer graphics, medical imaging, computer vision, data mining, and machine learning. Visualization is concerned with visually coding and communicating information. Many aspects of a visualization pipeline feature events of a probabilistic nature, bearing a striking resemblance to a communication pipeline. This Special Issue of Entropy focuses on the applications of information theory in visualization.

The holistic nature of information-theoretic reasoning has enabled many applications in visualization, including light source placement, view selection in mesh rendering, view selection in volume rendering, focus of attention in volume rendering, multiresolution volume visualization, feature highlighting in unsteady multi-field visualization, feature highlighting in time-varying volume visualization, transfer function design, multimodal data fusion, evaluating isosurfaces, measuring of observation capacity, measuring information content in multivariate data, and confirming the mathematical feasibility of visual multiplexing. Perhaps one of the most exciting applications is the potential to use information theory to underpin the discipline of visualization, i.e., to explain some or all observed phenomena or events in visualization, to provide effective abstraction and quantitative measurements of visual designs and visualization processes, and to enable processes and algorithms for modelling, predicting, and optimizing the effects of visualization.

This Special Issue of Entropy will be co-edited by Mateu Sbert, Min Chen and Han-Wei Shen, co-authors, together with Miquel Feixas, Ivan Viola and Anton Bardera of the recent CRC book, Information Theory for Visualization. Topics of interest include, but are not limited to:

1. Information-theoretic frameworks for

  • visualization in general;
  • a sub-domain of visualization, such as
    • volume visualization,
    • network visualization,
    • visualization-assisted machine learning,
    • interaction in visualization, and
    • empirical studies in visualization;
  • perception and cognition in visualization;
  • uncertainty visualization;
  • privacy-preserving visualization;
  • distribution-based data management and visualization.

2. Information-theoretic metrics in visualization, such as for measuring

  • abstraction;
  • aesthetics;
  • complexity of data, visualization, tasks and user spaces (alphabets);
  • cost-benefit of visualization processes;
  • distinguishability or similarity of visual objects (e.g., glyphs);
  • information preservation (or loss) of visual mapping;
  • salience in visualization;
  • uncertainty in visualization;
  • visualization capacities.

3. Information-theoretic algorithms, such as for

  • filtering and selection (e.g., isosurfacing, seeding);
  • grouping and clustering (e.g., edge bundling);
  • layout (e.g. clutter minimization);
  • view optimization;
  • feature extraction and tracking;
  • time-varying data;
  • multivariate visualization;
  • in situ visualization;
  • ensemble visualisation;
  • transfer function design.

Prof. Dr. Mateu  Sbert
Prof. Dr. Min  Chen
Prof. Dr. Han-Wei  Shen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • information theory
  • visualization
  • data science
  • visualization theories
  • metrics
  • algorithms

Published Papers (7 papers)

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Research

Open AccessArticle Dissecting Deep Learning Networks—Visualizing Mutual Information
Entropy 2018, 20(11), 823; https://doi.org/10.3390/e20110823
Received: 23 August 2018 / Revised: 22 October 2018 / Accepted: 23 October 2018 / Published: 26 October 2018
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Abstract
Deep Learning (DL) networks are recent revolutionary developments in artificial intelligence research. Typical networks are stacked by groups of layers that are further composed of many convolutional kernels or neurons. In network design, many hyper-parameters need to be defined heuristically before training in [...] Read more.
Deep Learning (DL) networks are recent revolutionary developments in artificial intelligence research. Typical networks are stacked by groups of layers that are further composed of many convolutional kernels or neurons. In network design, many hyper-parameters need to be defined heuristically before training in order to achieve high cross-validation accuracies. However, accuracy evaluation from the output layer alone is not sufficient to specify the roles of the hidden units in associated networks. This results in a significant knowledge gap between DL’s wider applications and its limited theoretical understanding. To narrow the knowledge gap, our study explores visualization techniques to illustrate the mutual information (MI) in DL networks. The MI is a theoretical measurement, reflecting the relationship between two sets of random variables even if their relationship is highly non-linear and hidden in high-dimensional data. Our study aims to understand the roles of DL units in classification performance of the networks. Via a series of experiments using several popular DL networks, it shows that the visualization of MI and its change patterns between the input/output with the hidden layers and basic units can facilitate a better understanding of these DL units’ roles. Our investigation on network convergence suggests a more objective manner to potentially evaluate DL networks. Furthermore, the visualization provides a useful tool to gain insights into the network performance, and thus to potentially facilitate the design of better network architectures by identifying redundancy and less-effective network units. Full article
(This article belongs to the Special Issue Information Theory Application in Visualization)
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Open AccessArticle An Information-Theoretic Framework for Evaluating Edge Bundling Visualization
Entropy 2018, 20(9), 625; https://doi.org/10.3390/e20090625
Received: 1 July 2018 / Revised: 14 August 2018 / Accepted: 18 August 2018 / Published: 21 August 2018
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Abstract
Edge bundling is a promising graph visualization approach to simplifying the visual result of a graph drawing. Plenty of edge bundling methods have been developed to generate diverse graph layouts. However, it is difficult to defend an edge bundling method with its resulting [...] Read more.
Edge bundling is a promising graph visualization approach to simplifying the visual result of a graph drawing. Plenty of edge bundling methods have been developed to generate diverse graph layouts. However, it is difficult to defend an edge bundling method with its resulting layout against other edge bundling methods as a clear theoretic evaluation framework is absent in the literature. In this paper, we propose an information-theoretic framework to evaluate the visual results of edge bundling techniques. We first illustrate the advantage of edge bundling visualizations for large graphs, and pinpoint the ambiguity resulting from drawing results. Second, we define and quantify the amount of information delivered by edge bundling visualization from the underlying network using information theory. Third, we propose a new algorithm to evaluate the resulting layouts of edge bundling using the amount of the mutual information between a raw network dataset and its edge bundling visualization. Comparison examples based on the proposed framework between different edge bundling techniques are presented. Full article
(This article belongs to the Special Issue Information Theory Application in Visualization)
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Open AccessArticle Information Guided Exploration of Scalar Values and Isocontours in Ensemble Datasets
Entropy 2018, 20(7), 540; https://doi.org/10.3390/e20070540
Received: 26 June 2018 / Revised: 16 July 2018 / Accepted: 18 July 2018 / Published: 20 July 2018
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Abstract
Uncertainty of scalar values in an ensemble dataset is often represented by the collection of their corresponding isocontours. Various techniques such as contour-boxplot, contour variability plot, glyphs and probabilistic marching-cubes have been proposed to analyze and visualize ensemble isocontours. All these techniques assume [...] Read more.
Uncertainty of scalar values in an ensemble dataset is often represented by the collection of their corresponding isocontours. Various techniques such as contour-boxplot, contour variability plot, glyphs and probabilistic marching-cubes have been proposed to analyze and visualize ensemble isocontours. All these techniques assume that a scalar value of interest is already known to the user. Not much work has been done in guiding users to select the scalar values for such uncertainty analysis. Moreover, analyzing and visualizing a large collection of ensemble isocontours for a selected scalar value has its own challenges. Interpreting the visualizations of such large collections of isocontours is also a difficult task. In this work, we propose a new information-theoretic approach towards addressing these issues. Using specific information measures that estimate the predictability and surprise of specific scalar values, we evaluate the overall uncertainty associated with all the scalar values in an ensemble system. This helps the scientist to understand the effects of uncertainty on different data features. To understand in finer details the contribution of individual members towards the uncertainty of the ensemble isocontours of a selected scalar value, we propose a conditional entropy based algorithm to quantify the individual contributions. This can help simplify analysis and visualization for systems with more members by identifying the members contributing the most towards overall uncertainty. We demonstrate the efficacy of our method by applying it on real-world datasets from material sciences, weather forecasting and ocean simulation experiments. Full article
(This article belongs to the Special Issue Information Theory Application in Visualization)
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Open AccessArticle A Survey of Viewpoint Selection Methods for Polygonal Models
Entropy 2018, 20(5), 370; https://doi.org/10.3390/e20050370
Received: 24 March 2018 / Revised: 11 May 2018 / Accepted: 11 May 2018 / Published: 16 May 2018
Cited by 1 | PDF Full-text (30998 KB) | HTML Full-text | XML Full-text
Abstract
Viewpoint selection has been an emerging area in computer graphics for some years, and it is now getting maturity with applications in fields such as scene navigation, scientific visualization, object recognition, mesh simplification, and camera placement. In this survey, we review and compare [...] Read more.
Viewpoint selection has been an emerging area in computer graphics for some years, and it is now getting maturity with applications in fields such as scene navigation, scientific visualization, object recognition, mesh simplification, and camera placement. In this survey, we review and compare twenty-two measures to select good views of a polygonal 3D model, classify them using an extension of the categories defined by Secord et al., and evaluate them against the Dutagaci et al. benchmark. Eleven of these measures have not been reviewed in previous surveys. Three out of the five short-listed best viewpoint measures are directly related to information. We also present in which fields the different viewpoint measures have been applied. Finally, we provide a publicly available framework where all the viewpoint selection measures are implemented and can be compared against each other. Full article
(This article belongs to the Special Issue Information Theory Application in Visualization)
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Open AccessArticle Viewpoint-Driven Simplification of Plant and Tree Foliage
Entropy 2018, 20(4), 213; https://doi.org/10.3390/e20040213
Received: 17 January 2018 / Revised: 16 March 2018 / Accepted: 16 March 2018 / Published: 21 March 2018
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Abstract
Plants and trees are an essential part of outdoor scenes. They are represented by such a vast number of polygons that performing real-time visualization is still a problem in spite of the advantages of the hardware. Some methods have appeared to solve this [...] Read more.
Plants and trees are an essential part of outdoor scenes. They are represented by such a vast number of polygons that performing real-time visualization is still a problem in spite of the advantages of the hardware. Some methods have appeared to solve this drawback based on point- or image-based rendering. However, geometry representation is required in some interactive applications. This work presents a simplification method that deals with the geometry of the foliage, reducing the number of primitives that represent these objects and making their interactive visualization possible. It is based on an image-based simplification that establishes an order of leaf pruning and reduces the complexity of the canopies of trees and plants. The proposed simplification method is viewpoint-driven and uses the mutual information in order to choose the leaf to prune. Moreover, this simplification method avoids the pruned appearance of the tree that is usually produced when a foliage representation is formed by a reduced number of leaves. The error introduced every time a leaf is pruned is compensated for if the size of the nearest leaf is altered to preserve the leafy appearance of the foliage. Results demonstrate the good quality and time performance of the presented work. Full article
(This article belongs to the Special Issue Information Theory Application in Visualization)
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Graphical abstract

Open AccessFeature PaperArticle IBVis: Interactive Visual Analytics for Information Bottleneck Based Trajectory Clustering
Entropy 2018, 20(3), 159; https://doi.org/10.3390/e20030159
Received: 11 January 2018 / Revised: 27 February 2018 / Accepted: 28 February 2018 / Published: 2 March 2018
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Abstract
Analyzing trajectory data plays an important role in practical applications, and clustering is one of the most widely used techniques for this task. The clustering approach based on information bottleneck (IB) principle has shown its effectiveness for trajectory data, in which a predefined [...] Read more.
Analyzing trajectory data plays an important role in practical applications, and clustering is one of the most widely used techniques for this task. The clustering approach based on information bottleneck (IB) principle has shown its effectiveness for trajectory data, in which a predefined number of the clusters and an explicit distance measure between trajectories are not required. However, presenting directly the final results of IB clustering gives no clear idea of both trajectory data and clustering process. Visual analytics actually provides a powerful methodology to address this issue. In this paper, we present an interactive visual analytics prototype called IBVis to supply an expressive investigation of IB-based trajectory clustering. IBVis provides various views to graphically present the key components of IB and the current clustering results. Rich user interactions drive different views work together, so as to monitor and steer the clustering procedure and to refine the results. In this way, insights on how to make better use of IB for different featured trajectory data can be gained for users, leading to better analyzing and understanding trajectory data. The applicability of IBVis has been evidenced in usage scenarios. In addition, the conducted user study shows IBVis is well designed and helpful for users. Full article
(This article belongs to the Special Issue Information Theory Application in Visualization)
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Open AccessFeature PaperArticle Rate-Distortion Theory for Clustering in the Perceptual Space
Entropy 2017, 19(9), 438; https://doi.org/10.3390/e19090438
Received: 7 July 2017 / Revised: 28 July 2017 / Accepted: 16 August 2017 / Published: 23 August 2017
Cited by 1 | PDF Full-text (2474 KB) | HTML Full-text | XML Full-text
Abstract
How to extract relevant information from large data sets has become a main challenge in data visualization. Clustering techniques that classify data into groups according to similarity metrics are a suitable strategy to tackle this problem. Generally, these techniques are applied in the [...] Read more.
How to extract relevant information from large data sets has become a main challenge in data visualization. Clustering techniques that classify data into groups according to similarity metrics are a suitable strategy to tackle this problem. Generally, these techniques are applied in the data space as an independent step previous to visualization. In this paper, we propose clustering on the perceptual space by maximizing the mutual information between the original data and the final visualization. With this purpose, we present a new information-theoretic framework based on the rate-distortion theory that allows us to achieve a maximally compressed data with a minimal signal distortion. Using this framework, we propose a methodology to design a visualization process that minimizes the information loss during the clustering process. Three application examples of the proposed methodology in different visualization techniques such as scatterplot, parallel coordinates, and summary trees are presented. Full article
(This article belongs to the Special Issue Information Theory Application in Visualization)
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Graphical abstract

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