Statistical Property Guided Feature Extraction for Volume Data

Feature visualization is of great significances in volume visualization, and feature extraction has been becoming extremely popular in feature visualization. While precise definition of features is usually absent which makes the extraction difficult. This paper employs probability density function (PDF) as statistical property, and proposes a statistical property guided approach to extract features for volume data. Basing on feature matching, it combines simple liner iterative cluster (SLIC) with Gaussian mixture model (GMM), and could do extraction without accurate feature definition. Further, GMM is paired with a normality test to reduce time cost and storage requirement. We demonstrate its applicability and superiority by successfully applying it on homogeneous and nonhomogeneous features. key words: feature extraction, probability density function (PDF), statistical property, simple liner iterative clustering (SLIC), Gaussian Mixture Model (GMM)

[1]  Lizhuang Ma,et al.  High-quality topological structure extraction of volumetric data on C2-continuous framework , 2015, Comput. Aided Geom. Des..

[2]  R. D'Agostino,et al.  A Suggestion for Using Powerful and Informative Tests of Normality , 1990 .

[3]  Kwan-Liu Ma,et al.  Importance-Driven Time-Varying Data Visualization , 2008, IEEE Transactions on Visualization and Computer Graphics.

[4]  Hans Hagen,et al.  Direct Feature Visualization Using Morse-Smale Complexes , 2012, IEEE Transactions on Visualization and Computer Graphics.

[5]  Jian Zhang,et al.  Efficient Volume Exploration Using the Gaussian Mixture Model. , 2011, IEEE transactions on visualization and computer graphics.

[6]  Han-Wei Shen,et al.  Efficient Local Statistical Analysis via Integral Histograms with Discrete Wavelet Transform , 2013, IEEE Transactions on Visualization and Computer Graphics.

[7]  Jeff A. Bilmes,et al.  A gentle tutorial of the em algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models , 1998 .

[8]  Abon Chaudhuri,et al.  Efficient Range Distribution Query for Visualizing Scientific Data , 2014, 2014 IEEE Pacific Visualization Symposium.

[9]  Nikolas P. Galatsanos,et al.  An Analytic Distance Metric for Gaussian Mixture Models with Application in Image Retrieval , 2005, ICANN.

[10]  Han-Wei Shen,et al.  Distribution Driven Extraction and Tracking of Features for Time-varying Data Analysis , 2016, IEEE Transactions on Visualization and Computer Graphics.

[11]  She Lihuang.  The Methodology of Evaluating Segmentation Algorithms on Medical Image , 2009 .

[12]  Pak Chung Wong,et al.  Feature Tracking and Visualization of the Madden-Julian Oscillation in Climate Simulation , 2013, IEEE Computer Graphics and Applications.

[13]  Pascal Fua,et al.  SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Jack Snoeyink,et al.  Computing contour trees in all dimensions , 2000, SODA '00.

[15]  Franz Sauer,et al.  Fast uncertainty-driven large-scale volume feature extraction on desktop PCs , 2015, 2015 IEEE 5th Symposium on Large Data Analysis and Visualization (LDAV).

[16]  Kenneth I. Joy,et al.  An Application of Multivariate Statistical Analysis for Query-Driven Visualization , 2011, IEEE Transactions on Visualization and Computer Graphics.

[17]  Marcus S. Day,et al.  Feature Tracking Using Reeb Graphs , 2011, Topological Methods in Data Analysis and Visualization.