The grand tour: a tool for viewing multidimensional data
SIAM Journal on Scientific and Statistical Computing
Graph drawing by force-directed placement
Software—Practice & Experience
The visible differences predictor: an algorithm for the assessment of image fidelity
Digital images and human vision
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Semi-automatic generation of transfer functions for direct volume rendering
VVS '98 Proceedings of the 1998 IEEE symposium on Volume visualization
Salient iso-surface detection with model-independent statistical signatures
Proceedings of the conference on Visualization '01
Efficient computation of the topology of level sets
Proceedings of the conference on Visualization '02
Feature Extraction and Iconic Visualization
IEEE Transactions on Visualization and Computer Graphics
Multidimensional Transfer Functions for Interactive Volume Rendering
IEEE Transactions on Visualization and Computer Graphics
Similarity Indexing with the SS-tree
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
A Bayesian Approach to Unsupervised One-Shot Learning of Object Categories
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Parallel coordinates: a tool for visualizing multi-dimensional geometry
VIS '90 Proceedings of the 1st conference on Visualization '90
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
An Intelligent System Approach to Higher-Dimensional Classification of Volume Data
IEEE Transactions on Visualization and Computer Graphics
Visualization of Boundaries in Volumetric Data Sets Using LH Histograms
IEEE Transactions on Visualization and Computer Graphics
Local Histograms for Design of Transfer Functions in Direct Volume Rendering
IEEE Transactions on Visualization and Computer Graphics
High-Level User Interfaces for Transfer Function Design with Semantics
IEEE Transactions on Visualization and Computer Graphics
Conjoint Analysis to Measure the Perceived Quality in Volume Rendering
IEEE Transactions on Visualization and Computer Graphics
Texture-based Transfer Functions for Direct Volume Rendering
IEEE Transactions on Visualization and Computer Graphics
Size-based Transfer Functions: A New Volume Exploration Technique
IEEE Transactions on Visualization and Computer Graphics
Data, Information, and Knowledge in Visualization
IEEE Computer Graphics and Applications
ClusterSculptor: A Visual Analytics Tool for High-Dimensional Data
VAST '07 Proceedings of the 2007 IEEE Symposium on Visual Analytics Science and Technology
PACIFICVIS '09 Proceedings of the 2009 IEEE Pacific Visualization Symposium
Design of multi-dimensional transfer functions using dimensional reduction
EUROVIS'07 Proceedings of the 9th Joint Eurographics / IEEE VGTC conference on Visualization
Generating time lines with virtual words for time-varying data visualization
Proceedings of the 5th International Symposium on Visual Information Communication and Interaction
Visualization and analysis of 3D time-varying simulations with time lines
Journal of Visual Languages and Computing
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The ever-growing arsenal of methods and parameters available for data visualization can be daunting to the casual user and even to domain experts. Furthermore, comprehensive expertise is often not available in a centralized venue, but distributed over sub-communities. As a means to overcome this inherent problem, efforts have begun to store visualization expertise directly with the visualization method and possibly the dataset, to then be utilized for user guidance in the data visualization, suggesting to the user both the visualization method and its best parameters for the data and task at hand. While this is certainly an immensely useful and promising development, one requirement remains - the matching of a newly acquired dataset with the appropriate segment of the library storing the expert knowledge. This requires one to detect and recognize the dataset's category at some level of granularity and then use this information as a library index. We describe a possible framework for accomplishing the first stage of this process, namely the data categorization, using data classification via a rich set of feature vectors sufficiently sensitive to detect critical variations. We demonstrate the utility of our framework by ways of a set of medical and computational datasets and visualize the resulting categorization as a layout in 2D.