Classifying visual knowledge representations: a foundation for visualization research
Jerry Lohse,Henry H. Rueter,Kevin Biolsi,Neff Walker +3 more
- pp 131-138
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TLDR
The authors discuss visual information processing issues relevant to the research, methodology and data analyses used to develop the classification system, results of the empirical study, and possible directions for future research.Abstract:
An exploratory effort to classify visual representations into homogeneous clusters is discussed. The authors collected hierarchical sorting data from twelve subjects. Five principal groups of visual representations emerged from a cluster analysis of sorting data: graphs and tables, maps, diagrams, networks, and icons. Two dimensions appear to distinguish these clusters: the amount of spatial information and cognitive processing effort. The authors discuss visual information processing issues relevant to the research, methodology and data analyses used to develop the classification system, results of the empirical study, and possible directions for future research. >read more
Citations
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Journal ArticleDOI
A classification of visual representations
TL;DR: The cognitive structure of graphics is examined and a structural classification of visual representations of graphs and images is reported to report, if visualization is to continue to advance as an interdisciplinary science, it must become more than a grab bag of techniques for displaying data.
Journal ArticleDOI
Towards a texture naming system: identifying relevant dimensions of texture.
TL;DR: An experiment to help identify the relevant higher order features of texture perceived by humans using the techniques of hierarchical cluster analysis, non-parametric multidimensional scaling (MDS), Classification and Regression Tree Analysis (CART), discriminant analysis, and principal component analysis.
Proceedings ArticleDOI
Towards a texture naming system: Identifying relevant dimensions of texture
A.R. Rao,G.L. Lohse +1 more
TL;DR: An experiment was designed to help identify the relevant higher order features of texture perceived by humans, and three orthogonal dimensions for texture to be repetitive vs. non-repetitive; high-contrast and non-directional vs. low-Contrast and directional.
Journal ArticleDOI
Review of Designs for Haptic Data Visualization
TL;DR: This review focuses on haptic methods that display data that can be used to present information, and consequently, the user gains quantitative, qualitative, or holistic knowledge about the presented data.
Journal ArticleDOI
Evaluating visualizations
E Morse,Michael Lewis,K.A Olsen +2 more
TL;DR: Methods for exhaustively testing the capabilities of a visualization by mapping from a domain-independent taxonomy of visual tasks to a specific domain, i.e. information retrieval, are discussed.
References
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Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis
TL;DR: The fundamental hypothesis is that dissimilarities and distances are monotonically related, and a quantitative, intuitively satisfying measure of goodness of fit is defined to this hypothesis.
Book
Vision: A Computational Investigation into the Human Representation and Processing of Visual Information
TL;DR: Marr's posthumously published Vision (1982) influenced a generation of brain and cognitive scientists, inspiring many to enter the field of visual perception as discussed by the authors, where the process of vision constructs a set of representations, starting from a description of the input image and culminating with three-dimensional objects in the surrounding environment, a central theme and one that has had farreaching influence in both neuroscience and cognitive science, is the notion of different levels of analysis.
Book
The Visual Display of Quantitative Information
TL;DR: The visual display of quantitative information is shown in the form of icons and symbols in order to facilitate the interpretation of data.