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

Authors:
Shoudong Han;Wenbing Tao;Xianglin Wu
Affiliations:
Institute of Systems Engineering, Huazhong University of Science and Technology, Wuhan 430074, China;School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China;Institute of Systems Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Venue:
Pattern Recognition
Year:
2011

Citing 20
Cited 3

Multiple Resolution Segmentation of Textured Images

IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Texture Segmentation Using Markov Random Field Models

IEEE Transactions on Pattern Analysis and Machine Intelligence
Multidimensional Orientation Estimation with Applications to Texture Analysis and Optical Flow

IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture Features for Browsing and Retrieval of Image Data

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients

International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Unsupervised Learning of Finite Mixture Models

IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast image segmentation based on multi-resolution analysis and wavelets

Pattern Recognition Letters
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues

IEEE Transactions on Pattern Analysis and Machine Intelligence
Lazy snapping

ACM SIGGRAPH 2004 Papers
"GrabCut": interactive foreground extraction using iterated graph cuts

ACM SIGGRAPH 2004 Papers
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Sparse Texture Representation Using Local Affine Regions

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Riemannian Framework for Tensor Computing

International Journal of Computer Vision
A study of Gaussian mixture models of color and texture features for image classification and segmentation

Pattern Recognition
k-means++: the advantages of careful seeding

SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Image segmentation based on GrabCut framework integrating multiscale nonlinear structure tensor

IEEE Transactions on Image Processing
Nonlinear structure tensors

Image and Vision Computing
Markov Random Field Texture Models

IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised texture segmentation with nonparametric neighborhood statistics

ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Efficient and reliable schemes for nonlinear diffusion filtering

IEEE Transactions on Image Processing

A nonparametric Riemannian framework on tensor field with application to foreground segmentation

Pattern Recognition
Automatic object extraction in nature scene based on visual saliency and super pixels

AICI'12 Proceedings of the 4th international conference on Artificial Intelligence and Computational Intelligence
Multilayer graph cuts based unsupervised color-texture image segmentation using multivariate mixed student's t-distribution and regional credibility merging

Pattern Recognition

Quantified Score

Hi-index	0.02

Visualization

Abstract

This paper proposes a novel texture segmentation approach using independent-scale component-wise Riemannian-covariance Gaussian mixture model (ICRGMM) in Kullback-Leibler (KL) measure based multi-scale nonlinear structure tensor (MSNST) space. We use the independent-scale distribution and full-covariance structure to replace the covariant-scale distribution and 1D-variance structure used in our previous research. To construct the optimal full-covariance structure, we define the full-covariance on KL, Euclidean, log-Euclidean, and Riemannian gradient mappings, and compare their performances. The comparison experiments demonstrate that the Riemannian gradient mapping leads to its optimum properties over other choices when constructing the full-covariance. To estimate and update the statistical parameters more accurately, the component-wise expectation-maximization for mixtures (CEM^2) algorithm is proposed instead of the originally used K-means algorithm. The superiority of the proposed ICRGMM has been demonstrated based on texture clustering and Graph Cuts based texture segmentation using a large number of synthesis texture images and real natural scene textured images, and further analyzed in terms of error ratio and modified F-measure, respectively.