Statistical Property Guided Feature Extraction for Volume Data

Li WANG  Xiaoan TANG  Junda ZHANG  Dongdong GUAN  

IEICE TRANSACTIONS on Information and Systems   Vol.E101-D   No.1   pp.261-264
Publication Date: 2018/01/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2017EDL8188
Type of Manuscript: LETTER
Category: Pattern Recognition
feature extraction,  probability density function (PDF),  statistical property,  simple liner iterative clustering (SLIC),  Gaussian Mixture Model (GMM),  

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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 non-homogeneous features.