Texture segmentation using independent-scale component-wise Riemannian-covariance Gaussian mixture model in KL measure based multi-scale nonlinear structure tensor space

Pattern Recognition

Volumn 44, Issue 3, 2011, Pages 503-518

  Han, Shoudong ^a   Tao, Wenbing ^a   Wu, Xianglin ^a  

^a HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

Graph Cuts; Independent scale component wise Riemannian covariance Gaussian mixture model (ICRGMM); Multi scale nonlinear structure tensor (MSNST); Texture segmentation

Indexed keywords

COMPONENTWISE; COVARIANCE STRUCTURES; COVARIANT; ERROR RATIO; EUCLIDEAN; EXPECTATION MAXIMIZATION; F-MEASURE; GAUSSIAN MIXTURE MODEL; GRAPH CUT; K-MEANS ALGORITHM; KULLBACK-LEIBLER; LOG-EUCLIDEAN; MULTISCALES; NATURAL SCENES; NONLINEAR STRUCTURE TENSOR; OPTIMUM PROPERTIES; RIEMANNIAN GRADIENT; SCALE DISTRIBUTIONS; STATISTICAL PARAMETERS; TEXTURE IMAGE; TEXTURE SEGMENTATION; TEXTURED IMAGES; VARIANCE STRUCTURE;

BLIND SOURCE SEPARATION; GAUSSIAN DISTRIBUTION; GRAPHIC METHODS; OPTIMIZATION; TENSORS; TEXTURES;

IMAGE SEGMENTATION;

EID: 78649322646     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2010.09.006     Document Type: Article

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