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

DOI10.1016/J.PATCOG.2010.09.006zbMath1207.68282OpenAlexW2063287010WikidataQ115341837 ScholiaQ115341837MaRDI QIDQ621981

Xianglin Wu, Wenbing Tao, Shoudong Han

Publication date: 31 January 2011

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.patcog.2010.09.006

zbMATH Keywords

texture segmentationgraph cutsICRGMMmulti-scale nonlinear structure tensor

Mathematics Subject Classification ID

Pattern recognition, speech recognition (68T10)

Related Items (2)

\(L^\alpha \) Riemannian weighted centers of mass applied to compose an image filter to diffusion tensor imaging ⋮ A nonparametric Riemannian framework on tensor field with application to foreground segmentation

Uses Software

• MAXFLOW
• BSDS
• Steerable pyramid
• GrabCut
• k-means++

Cites Work

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• A parametric texture model based on joint statistics of complex wavelet coefficients
• A study of Gaussian mixture models of color and texture features for image classification and segmentation
• A Riemannian framework for tensor computing
• Image Segmentation Based on GrabCut Framework Integrating Multiscale Nonlinear Structure Tensor
• Computer Vision - ECCV 2004

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