17 research outputs found
Automatic interpretation of map visualizations with color-encoded scalar values from bitmap images
Las visualizaciones de mapas son usadas en diferentes a 鈥榬eas para mostrar datos geogr谩ficos (por ejemplo, datos climatol贸gicos u oceanogr谩ficos, resultados de an谩lisis empresariales, entre otros). Estas visualizaciones se pueden encontrar en art铆culos de noticias, art铆culos cient铆ficos y en la Web; sin embargo, muchas de ellas est谩n disponibles como im谩genes en mapa de bits, lo que dificulta que el computador interprete los datos visualizados para su indexaci贸n y reutilizaci贸n.
En este trabajo proponemos una secuencia de pasos para recuperar la codificaci贸n visual a partir de im谩genes en mapa de bits de mapas geogr谩ficos que utilizan el color para codificar los valores de los datos. Nuestros resultados fueron analizados usando mapas extra铆dos de documentos cient铆ficos, logrando una alta precisi贸n en cada paso propuesto. Adicionalmente presentamos a iGeoMap, nuestro sistema web que utiliza la codificaci贸n visual extra铆da para permitir la interacci贸n del usuario sobre im谩genes en mapa de bits de visualizaciones de mapas.Trabajo de investigaci贸
Towards automated infographic design: Deep learning-based auto-extraction of extensible timeline
Designers need to consider not only perceptual effectiveness but also visual
styles when creating an infographic. This process can be difficult and time
consuming for professional designers, not to mention non-expert users, leading
to the demand for automated infographics design. As a first step, we focus on
timeline infographics, which have been widely used for centuries. We contribute
an end-to-end approach that automatically extracts an extensible timeline
template from a bitmap image. Our approach adopts a deconstruction and
reconstruction paradigm. At the deconstruction stage, we propose a multi-task
deep neural network that simultaneously parses two kinds of information from a
bitmap timeline: 1) the global information, i.e., the representation, scale,
layout, and orientation of the timeline, and 2) the local information, i.e.,
the location, category, and pixels of each visual element on the timeline. At
the reconstruction stage, we propose a pipeline with three techniques, i.e.,
Non-Maximum Merging, Redundancy Recover, and DL GrabCut, to extract an
extensible template from the infographic, by utilizing the deconstruction
results. To evaluate the effectiveness of our approach, we synthesize a
timeline dataset (4296 images) and collect a real-world timeline dataset (393
images) from the Internet. We first report quantitative evaluation results of
our approach over the two datasets. Then, we present examples of automatically
extracted templates and timelines automatically generated based on these
templates to qualitatively demonstrate the performance. The results confirm
that our approach can effectively extract extensible templates from real-world
timeline infographics.Comment: 10 pages, Automated Infographic Design, Deep Learning-based Approach,
Timeline Infographics, Multi-task Mode
Searching the Visual Style and Structure of D3 Visualizations
We present a search engine for D3 visualizations that allows queries based on
their visual style and underlying structure. To build the engine we crawl a
collection of 7860 D3 visualizations from the Web and deconstruct each one to
recover its data, its data-encoding marks and the encodings describing how the
data is mapped to visual attributes of the marks. We also extract axes and
other non-data-encoding attributes of marks (e.g., typeface, background color).
Our search engine indexes this style and structure information as well as
metadata about the webpage containing the chart. We show how visualization
developers can search the collection to find visualizations that exhibit
specific design characteristics and thereby explore the space of possible
designs. We also demonstrate how researchers can use the search engine to
identify commonly used visual design patterns and we perform such a demographic
design analysis across our collection of D3 charts. A user study reveals that
visualization developers found our style and structure based search engine to
be significantly more useful and satisfying for finding different designs of D3
charts, than a baseline search engine that only allows keyword search over the
webpage containing a chart
KB4VA: A Knowledge Base of Visualization Designs for Visual Analytics
Visual analytics (VA) systems have been widely used to facilitate
decision-making and analytical reasoning in various application domains. VA
involves visual designs, interaction designs, and data mining, which is a
systematic and complex paradigm. In this work, we focus on the design of
effective visualizations for complex data and analytical tasks, which is a
critical step in designing a VA system. This step is challenging because it
requires extensive knowledge about domain problems and visualization to design
effective encodings. Existing visualization designs published in top venues are
valuable resources to inspire designs for problems with similar data structures
and tasks. However, those designs are hard to understand, parse, and retrieve
due to the lack of specifications. To address this problem, we build KB4VA, a
knowledge base of visualization designs in VA systems with comprehensive labels
about their analytical tasks and visual encodings. Our labeling scheme is
inspired by a workshop study with 12 VA researchers to learn user requirements
in understanding and retrieving professional visualization designs in VA
systems. The theme extends Vega-Lite specifications for describing advanced and
composited visualization designs in a declarative manner, thus facilitating
human understanding and automatic indexing. To demonstrate the usefulness of
our knowledge base, we present a user study about design inspirations for VA
tasks. In summary, our work opens new perspectives for enhancing the
accessibility and reusability of professional visualization designs
A Survey on ML4VIS: Applying Machine Learning Advances to Data Visualization
Inspired by the great success of machine learning (ML), researchers have
applied ML techniques to visualizations to achieve a better design,
development, and evaluation of visualizations. This branch of studies, known as
ML4VIS, is gaining increasing research attention in recent years. To
successfully adapt ML techniques for visualizations, a structured understanding
of the integration of ML4VISis needed. In this paper, we systematically survey
88 ML4VIS studies, aiming to answer two motivating questions: "what
visualization processes can be assisted by ML?" and "how ML techniques can be
used to solve visualization problems?" This survey reveals seven main processes
where the employment of ML techniques can benefit visualizations:Data
Processing4VIS, Data-VIS Mapping, InsightCommunication, Style Imitation, VIS
Interaction, VIS Reading, and User Profiling. The seven processes are related
to existing visualization theoretical models in an ML4VIS pipeline, aiming to
illuminate the role of ML-assisted visualization in general
visualizations.Meanwhile, the seven processes are mapped into main learning
tasks in ML to align the capabilities of ML with the needs in visualization.
Current practices and future opportunities of ML4VIS are discussed in the
context of the ML4VIS pipeline and the ML-VIS mapping. While more studies are
still needed in the area of ML4VIS, we hope this paper can provide a
stepping-stone for future exploration. A web-based interactive browser of this
survey is available at https://ml4vis.github.ioComment: 19 pages, 12 figures, 4 table
InvVis: Large-Scale Data Embedding for Invertible Visualization
We present InvVis, a new approach for invertible visualization, which is
reconstructing or further modifying a visualization from an image. InvVis
allows the embedding of a significant amount of data, such as chart data, chart
information, source code, etc., into visualization images. The encoded image is
perceptually indistinguishable from the original one. We propose a new method
to efficiently express chart data in the form of images, enabling
large-capacity data embedding. We also outline a model based on the invertible
neural network to achieve high-quality data concealing and revealing. We
explore and implement a variety of application scenarios of InvVis.
Additionally, we conduct a series of evaluation experiments to assess our
method from multiple perspectives, including data embedding quality, data
restoration accuracy, data encoding capacity, etc. The result of our
experiments demonstrates the great potential of InvVis in invertible
visualization.Comment: IEEE VIS 202
SeeChart: Enabling Accessible Visualizations Through Interactive Natural Language Interface For People with Visual Impairments
Web-based data visualizations have become very popular for exploring data and
communicating insights. Newspapers, journals, and reports regularly publish
visualizations to tell compelling stories with data. Unfortunately, most
visualizations are inaccessible to readers with visual impairments. For many
charts on the web, there are no accompanying alternative (alt) texts, and even
if such texts exist they do not adequately describe important insights from
charts. To address the problem, we first interviewed 15 blind users to
understand their challenges and requirements for reading data visualizations.
Based on the insights from these interviews, we developed SeeChart, an
interactive tool that automatically deconstructs charts from web pages and then
converts them to accessible visualizations for blind people by enabling them to
hear the chart summary as well as to interact through data points using the
keyboard. Our evaluation with 14 blind participants suggests the efficacy of
SeeChart in understanding key insights from charts and fulfilling their
information needs while reducing their required time and cognitive burden.Comment: 28 pages, 13 figure