ScagCNN: Estimating Visual Characterizations of 2D Scatterplots via Convolution Neural Network

Scagnostics is a set of visual features that characterizes the data distribution of a 2D scatterplot and has been used in a wide range of applications. However, calculating the scagnostics scores involves computationally expensive algorithms. Moreover, the algorithms are sensitive to the slight changes in the underlying data distribution within the scatterplot. Therefore, this work provides a machine learning model, called ScagCNN, to estimate the scagnostics scores. This model aims to improve the scagnostics computation time and to reduce the sensitivity to the small shifts in the data distribution. This work also provides a web prototype to explore the predictive performance of the model and to give a visual explanation about whether a prediction is accurate. Furthermore, we test the performance of our solution on datasets of various sizes.

[1]  A. Shapiro,et al.  National Consortium for the Study of Terrorism and Responses to Terrorism , 2010 .

[2]  Jeffrey Heer,et al.  SpanningAspectRatioBank Easing FunctionS ArrayIn ColorIn Date Interpolator MatrixInterpola NumObjecPointI Rectang ISchedu Parallel Pause Scheduler Sequen Transition Transitioner Transiti Tween Co DelimGraphMLCon IData JSONCon DataField DataSc Dat DataSource Data DataUtil DirtySprite LineS RectSprite , 2011 .

[3]  Ramakant Nevatia,et al.  Face recognition using deep multi-pose representations , 2016, 2016 IEEE Winter Conference on Applications of Computer Vision (WACV).

[4]  Michaël Aupetit,et al.  Data‐driven Evaluation of Visual Quality Measures , 2015, Comput. Graph. Forum.

[5]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[6]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[7]  David G. Kirkpatrick,et al.  On the shape of a set of points in the plane , 1983, IEEE Trans. Inf. Theory.

[8]  Ulrik Brandes,et al.  Quality Metrics for Information Visualization , 2018, Comput. Graph. Forum.

[9]  Robert L. Grossman,et al.  High-Dimensional Visual Analytics: Interactive Exploration Guided by Pairwise Views of Point Distributions , 2006, IEEE Transactions on Visualization and Computer Graphics.

[10]  Julianna M Czum,et al.  Dive Into Deep Learning. , 2020, Journal of the American College of Radiology : JACR.

[11]  Wei Chen,et al.  ScatterNet: A Deep Subjective Similarity Model for Visual Analysis of Scatterplots , 2020, IEEE Transactions on Visualization and Computer Graphics.

[12]  Stefano Tarantola,et al.  Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models , 2004 .

[13]  R. Grossman,et al.  Graph-theoretic scagnostics , 2005, IEEE Symposium on Information Visualization, 2005. INFOVIS 2005..

[14]  HeerJeffrey,et al.  D3 Data-Driven Documents , 2011 .

[15]  Vung Pham,et al.  Outliagnostics: Visualizing Temporal Discrepancy in Outlying Signatures of Data Entries , 2019, 2019 IEEE Visualization in Data Science (VDS).

[16]  Leland Wilkinson,et al.  Scagnostics Distributions , 2008 .

[17]  Giuseppe Santucci,et al.  Quality Metrics for 2D Scatterplot Graphics: Automatically Reducing Visual Clutter , 2004, Smart Graphics.

[18]  Vung Pham,et al.  Visualization and Explainable Machine Learning for Efficient Manufacturing and System Operations , 2019, Smart and Sustainable Manufacturing Systems.

[19]  Michaël Aupetit,et al.  SepMe: 2002 New visual separation measures , 2016, 2016 IEEE Pacific Visualization Symposium (PacificVis).

[20]  François Chollet,et al.  Keras: The Python Deep Learning library , 2018 .

[21]  Giuseppe Santucci,et al.  Visual quality metrics , 2006, BELIV '06.

[22]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[23]  Leland Wilkinson,et al.  Transforming Scagnostics to Reveal Hidden Features , 2014, IEEE Transactions on Visualization and Computer Graphics.

[24]  Vung Pham,et al.  MTSAD: Multivariate Time Series Abnormality Detection and Visualization , 2019, 2019 IEEE International Conference on Big Data (Big Data).

[25]  Vung Pham,et al.  ScagnosticsJS: Extended Scatterplot Visual Features for the Web , 2020, Eurographics.

[26]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[27]  Simon Haykin,et al.  GradientBased Learning Applied to Document Recognition , 2001 .

[28]  Oliver Deussen,et al.  Improving the Robustness of Scagnostics , 2020, IEEE Transactions on Visualization and Computer Graphics.