TY - JOUR
T1 - GlyphCreator
T2 - Towards Example-based Automatic Generation of Circular Glyphs
AU - Ying, Lu
AU - Tangl, Tan
AU - Luo, Yuzhe
AU - Shen, Lvkeshen
AU - Xie, Xiao
AU - Yu, Lingyun
AU - Wu, Yingcai
N1 - Publisher Copyright:
© 1995-2012 IEEE.
PY - 2022/1/1
Y1 - 2022/1/1
N2 - Circular glyphs are used across disparate fields to represent multidimensional data. However, although these glyphs are extremely effective, creating them is often laborious, even for those with professional design skills. This paper presents GlyphCreator, an interactive tool for the example-based generation of circular glyphs. Given an example circular glyph and multidimensional input data, GlyphCreator promptly generates a list of design candidates, any of which can be edited to satisfy the requirements of a particular representation. To develop GlyphCreator, we first derive a design space of circular glyphs by summarizing relationships between different visual elements. With this design space, we build a circular glyph dataset and develop a deep learning model for glyph parsing. The model can deconstruct a circular glyph bitmap into a series of visual elements. Next, we introduce an interface that helps users bind the input data attributes to visual elements and customize visual styles. We evaluate the parsing model through a quantitative experiment, demonstrate the use of GlyphCreator through two use scenarios, and validate its effectiveness through user interviews.
AB - Circular glyphs are used across disparate fields to represent multidimensional data. However, although these glyphs are extremely effective, creating them is often laborious, even for those with professional design skills. This paper presents GlyphCreator, an interactive tool for the example-based generation of circular glyphs. Given an example circular glyph and multidimensional input data, GlyphCreator promptly generates a list of design candidates, any of which can be edited to satisfy the requirements of a particular representation. To develop GlyphCreator, we first derive a design space of circular glyphs by summarizing relationships between different visual elements. With this design space, we build a circular glyph dataset and develop a deep learning model for glyph parsing. The model can deconstruct a circular glyph bitmap into a series of visual elements. Next, we introduce an interface that helps users bind the input data attributes to visual elements and customize visual styles. We evaluate the parsing model through a quantitative experiment, demonstrate the use of GlyphCreator through two use scenarios, and validate its effectiveness through user interviews.
KW - Glyph-based visualization
KW - automatic visualization
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85118638951&partnerID=8YFLogxK
U2 - 10.1109/TVCG.2021.3114877
DO - 10.1109/TVCG.2021.3114877
M3 - Article
C2 - 34596552
AN - SCOPUS:85118638951
SN - 1077-2626
VL - 28
SP - 400
EP - 410
JO - IEEE Transactions on Visualization and Computer Graphics
JF - IEEE Transactions on Visualization and Computer Graphics
IS - 1
ER -