Including Spatial Information in Clustering of Multi-Channel Images

A huge number of clustering methods have been applied to many different kinds of data set including multivariate images, such as magnetic resonance images and remote sensing images. However, not many methods include spatial information of the image data. In this tutorial, the major types of clustering techniques are summarized. Particular attention will be devoted to the extension of clustering techniques to take into account both spectral and spatial information of the multivariate image data. General guidelines for the optimal use of these algorithms are given. The application of preand post-processing methods is also discussed.

[1]  J. Xu.  Knowledge Discovery and Data Mining , 2014, Computing Handbook, 3rd ed..

[2]  P. Deb.  Finite Mixture Models , 2008 .

[3]  A. Vinciarelli,et al.  Application of information retrieval techniques to single writer documents , 2005, Pattern Recognit. Lett..

[4]  Adrian E. Raftery,et al.  Incremental Model-Based Clustering for Large Datasets With Small Clusters , 2005 .

[5]  Lutgarde M. C. Buydens,et al.  Clustering multispectral images: a tutorial , 2005 .

[6]  David W. Paglieroni,et al.  Convergent coarseness regulation for segmented images , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

[7]  Joachim M. Buhmann,et al.  Boundary-constrained agglomerative segmentation , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Adrian E. Raftery,et al.  Model-Based Clustering for Image Segmentation and Large Datasets via Sampling , 2004, J. Classif..

[9]  Laurent Ferro-Famil,et al.  Unsupervised terrain classification preserving polarimetric scattering characteristics , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[10]  Dirk H. Hoekman,et al.  A new polarimetric classification approach evaluated for agricultural crops , 2003, IEEE Trans. Geosci. Remote. Sens..

[11]  M. Hazelton.  Variable kernel density estimation , 2003 .

[12]  Florence Tupin,et al.  Unsupervised classification of radar images using hidden Markov chains and hidden Markov random fields , 2003, IEEE Trans. Geosci. Remote. Sens..

[13]  Adrian E. Raftery,et al.  Approximate Bayes Factors for Image Segmentation: The Pseudolikelihood Information Criterion (PLIC) , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Qiong Jackson,et al.  Adaptive Bayesian contextual classification based on Markov random fields , 2002, IEEE International Geoscience and Remote Sensing Symposium.

[15]  Beata Walczak,et al.  Hierarchical clustering extended with visual complements of environmental data set , 2002 .

[16]  J. C. Noordam,et al.  Multivariate image segmentation with cluster size insensitive fuzzy C-means , 2002 .

[17]  Vikash Kumar,et al.  A MRF model-based segmentation approach to classification for multispectral imagery , 2002, IEEE Trans. Geosci. Remote. Sens..

[18]  Adrian E. Raftery,et al.  Model-Based Clustering, Discriminant Analysis, and Density Estimation , 2002 .

[19]  Jie Liang,et al.  Computational analysis of microarray gene expression profiles: clustering, classification, and beyond , 2002 .

[20]  Shaoping Ma,et al.  Correlation-Based Web Document Clustering for Adaptive Web Interface Design , 2002, Knowledge and Information Systems.

[21]  C. Posse.  Hierarchical Model-Based Clustering for Large Datasets , 2001 .

[22]  Mausumi Acharyya,et al.  An adaptive approach to unsupervised texture segmentation using M-Band wavelet transform , 2001, Signal Process..

[23]  Karl Mosler,et al.  A Cautionary Note on Likelihood Ratio Tests in Mixture Models , 2000 .

[24]  Thomas L. Ainsworth,et al.  Unsupervised classification using polarimetric decomposition and the complex Wishart classifier , 1999, IEEE Trans. Geosci. Remote. Sens..

[25]  Pekka Teppola,et al.  Adaptive Fuzzy C-Means clustering in process monitoring , 1999 .

[26]  Chris Fraley,et al.  Algorithms for Model-Based Gaussian Hierarchical Clustering , 1998, SIAM J. Sci. Comput..

[27]  Daniel A. Keim,et al.  An Efficient Approach to Clustering in Large Multimedia Databases with Noise , 1998, KDD.

[28]  D. Comaniciu,et al.  Distribution Free Decomposition of Multivariate Data , 1998, Pattern Analysis & Applications.

[29]  Stephen L. Durden,et al.  A three-component scattering model for polarimetric SAR data , 1998, IEEE Trans. Geosci. Remote. Sens..

[30]  Silvana G. Dellepiane,et al.  Synthetic aperture radar image segmentation by a detail preserving Markov random field approach , 1997, IEEE Trans. Geosci. Remote. Sens..

[31]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[32]  Patrick Bouthemy,et al.  Computation and analysis of image motion: A synopsis of current problems and methods , 1996, International Journal of Computer Vision.

[33]  Yizong Cheng,et al.  Mean Shift, Mode Seeking, and Clustering , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[34]  Ron Kwok,et al.  Classification of multi-look polarimetric SAR imagery based on complex Wishart distribution , 1994 .

[35]  Richard G. Brereton,et al.  Multivariate Pattern Recognition in Chemometrics: Illustrated by Case Studies , 1992 .

[36]  W. Qian,et al.  Estimation of parameters in hidden Markov models , 1991, Philosophical Transactions of the Royal Society of London. Series A: Physical and Engineering Sciences.

[37]  L. Novak,et al.  Bayes classification of terrain cover using normalized polarimetric data , 1988 .

[38]  Subir Ghosh,et al.  Statistical Analysis With Missing Data , 1988 .

[39]  P. J. Green,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

[40]  J. Besag.  On the Statistical Analysis of Dirty Pictures , 1986 .

[41]  David J. Hand,et al.  Discrimination and Classification , 1982 .

[42]  Desire L. Massart,et al.  Potential methods in pattern recognition : Part 2. CLUPOT —an unsupervised pattern recognition technique , 1981 .

[43]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[44]  G. Schwarz.  Estimating the Dimension of a Model , 1978 .

[45]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[46]  Ray A. Jarvis,et al.  Clustering Using a Similarity Measure Based on Shared Near Neighbors , 1973, IEEE Transactions on Computers.

[47]  C. Quesenberry,et al.  A nonparametric estimate of a multivariate density function , 1965 .

[48]  Geoffrey H. Ball,et al.  ISODATA, A NOVEL METHOD OF DATA ANALYSIS AND PATTERN CLASSIFICATION , 1965 .

[49]  P. Sivakumar,et al.  A REVIEW ON IMAGE SEGMENTATION TECHNIQUES , 2016 .

[50]  李玉雲.  Curriculum Vitae , 2015, Obere Extremität.

[51]  A. W. Simonetti,et al.  Investigation of brain tumor classification and its reliability using chemometrics on MR spectroscopy and MR imaging data , 2004 .

[52]  Levent Ertoz,et al.  A New Shared Nearest Neighbor Clustering Algorithm and its Applications , 2002 .

[53]  Lutgarde M. C. Buydens,et al.  Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/cem.721 Mixture modelling of medical magnetic resonance data , 2002 .

[54]  Padhraic Smyth,et al.  Model selection for probabilistic clustering using cross-validated likelihood , 2000, Stat. Comput..

[55]  Padhraic Smyth,et al.  Modeling of Inhomogeneous Markov Random Fields with Applications to Cloud Screening , 1998 .

[56]  Eric Pottier,et al.  An entropy based classification scheme for land applications of polarimetric SAR , 1997, IEEE Trans. Geosci. Remote. Sens..

[57]  Anil K. Jain,et al.  A Markov random field model for classification of multisource satellite imagery , 1996, IEEE Trans. Geosci. Remote. Sens..

[58]  Adrian E. Raftery,et al.  Bayes factors and model uncertainty , 1995 .

[59]  A. Raftery,et al.  Bayes factors , 1995 .

[60]  R. Grieken,et al.  Hierarchical cluster analysis with stopping rules built on Akaike's information criterion for aerosol particle classification based on electron probe X-ray microanalysis , 1994 .

[61]  Belur V. Dasarathy,et al.  Nearest neighbor (NN) norms: NN pattern classification techniques , 1991 .

[62]  J. Zyl,et al.  Unsupervised classification of scattering behavior using radar polarimetry data , 1989 .

[63]  John A. Richards,et al.  Remote Sensing Digital Image Analysis , 1986 .

[64]  Thomas S. Huang,et al.  Image Sequence Analysis: Motion Estimation , 1981 .

[65]  Larry D. Hostetler,et al.  The estimation of the gradient of a density function, with applications in pattern recognition , 1975, IEEE Trans. Inf. Theory.

[66]  J. Besag.  Spatial Interaction and the Statistical Analysis of Lattice Systems , 1974 .