8.3.4 Classification Methods, Clustering for Region Segmentation

Chapter Contents (Back)
Classification. Pattern Recognition. Segmentation, Clustering. Clustering. Classification for segmentation. See also Pattern Recognition, General Issues.

Haralick, R.M., and Kelly, G.,
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Eklundh, J.O., Yamamoto, H., and Rosenfeld, A.,
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PAMI(2), No. 1, January 1980, pp. 72-75. BibRef 8001
Earlier:
Relaxation Methods in Multispectral Pixel Classification,
UMD-TR-662, July 1978. Relaxation. Segmentation, Color. BibRef

Eklundh, J.O., Lansner, A., and Wessblad, R.,
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Eklundh, J.O.,
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Dondes, P.A., and Rosenfeld, A.,
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Blanz, W.E., Reinhardt, E.R.,
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Sclove, S.L.,
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And: Reply to Comments: PAMI(6), No. 5, September 1984, pp. 657-658. BibRef

Titterington, D.M.,
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Huntsberger, T.L., Jacobs, C.L., Cannon, R.L.,
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Markham, K.C.,
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Amadasun, M., King, R.A.,
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Geong, D.S., and Lapsa, P.M.,
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Zhang, J., and Modestino, J.W.,
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Langan, D.A., Modestino, J.W., Zhang, J.,
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IP(7), No. 2, February 1998, pp. 180-195.
IEEE DOI 9802
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Earlier: ICIP94(II: 197-201).
IEEE DOI 9411
See also Maximum-Likelihood Parameter Estimation for Unsupervised Stochastic Model-Based Image Segmentation. BibRef

Jolion, J.M.[Jean-Michel], Meer, P.[Peter], and Bataouche, S.[Samira],
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Jolion, J.M.[Jean-Michel], Meer, P.[Peter], and Rosenfeld, A.[Azriel],
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Bataouche, S.[Samira], and Jolion, J.M.[Jean-Michel],
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IAP(510-517), 1989. BibRef 8900

Pla, F., Juste, F., Ferri, F.J., and Vicens, M.,
Colour Segmentation Based on a Light Reflection Model to Locate Citrus Fruits for Robotic Harvesting,
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Ng, I., Kittler, J.V., Illingworth, J.,
Supervised Segmentation Using a Multiresolution Data Representation,
SP(31), 1993, pp. 133-163. BibRef 9300
Earlier: BMVC91(xx-yy).
PDF File. 9109
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Uchiyama, T., and Arbib, M.A.,
Color Image Segmentation Using Competitive Learning,
PAMI(16), No. 12, December 1994, pp. 1197-1206.
IEEE DOI BibRef 9412
Earlier:
Object Extraction System from a Color Image,
IAS93(xx-yy). Generate clusters in color space. BibRef

Schroeter, P., Bigün, J.,
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PR(28), No. 5, May 1995, pp. 695-709.
WWW Link. Problems due to different size regions. BibRef 9505

Pappas, T.N., and Jayant, N.S.,
An Adaptive Clustering Algorithm for Image Segmentation,
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Krishnapuram, R., and Freg, C.P.,
Fuzzy Algorithms to Find Linear and Planar Clusters and Their Application,
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Panda, D.P.,
Segmentation of FLIR Images by Pixel Classification,
UMD-CS TR-508, February 1977. BibRef 7702
And: DARPA77(65-70). Segmentation as a point wise classification problem. Set of features and define decision surface; gray level and edge values together <== using a joint histogram (2-D); valleys selected manually. BibRef

Therrien, C.W.[Charles W.],
An Estimation-Theoretic Approach to Terrain Image Segmentation,
CVGIP(22), No. 3, June 1983, pp. 313-326.
WWW Link. BibRef 8306
Earlier:
Linear Filtering Models for Texture Classification and Segmentation,
ICPR80(1132-1135). Segmentation based on texture classification (pick sample areas to get statistics) maximum likelihood is poor, maximum a posteriori estimation is better. BibRef

Therrien, C.W.,
Multi-channel Filtering Methods for Segmentation of Color Images,
CVPR85(637-639). (Naval Postgraduate School) Obvious. BibRef 8500

Zhang, M.C., Haralick, R.M., Campbell, J.B.,
Multispectral Image Context Classification Using Stochastic Relaxation,
SMC(20), 1990, pp. 128-140. BibRef 9000

Knapman, J., Dickson, W.,
Hierarchical Probabilistic Image Segmentation,
IVC(12), No. 7, September 1994, pp. 447-457.
WWW Link. BibRef 9409

Zheng, Y.J.,
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MVA(8), No. 5, 1995, pp. 262-274.
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Simpson, J.J., Keller, R.H.,
An Improved Fuzzy-Logic Segmentation of Sea-Ice, Clouds, and Ocean in Remotely-Sensed Arctic Imagery,
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Olariu, S., Rao, N.S.V.,
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Rao, N.S.V., Oblow, E.M., Glover, C.W.,
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Earlier: ICPR92(II:603-606).
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BibRef

Salzenstein, F., Pieczynski, W.,
Parameter-Estimation in Hidden Fuzzy Markov Random-Fields and Image Segmentation,
GMIP(59), No. 4, July 1997, pp. 205-220. 9709
See also Estimation of Generalized Mixtures and Its Application in Image Segmentation. BibRef

Salzenstein, F., Collet, C.,
Fuzzy Markov Random Fields versus Chains for Multispectral Image Segmentation,
PAMI(28), No. 11, November 2006, pp. 1753-1767.
IEEE DOI 0609
Comparison of fuzzy Markov chain with fuzzy random field models. BibRef

Salzenstein, F., Collet, C., Lecam, S., Hatt, M.,
Non-stationary fuzzy Markov chain,
PRL(28), No. 16, December 2007, pp. 2201-2208.
WWW Link. 0711
Fuzzy Markov chain; Triplet Markov chain; Non-stationary chain; Multispectral image segmentation BibRef

Flitti, F., Collet, C.,
Markovian regularization of latent-variable-models mixture for New multi-component image reduction/segmentation scheme,
SIViP(1), No. 3, August 2007, pp. 191-201.
Springer DOI 0803
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Pieczynski, W.[Wojciech],
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Pieczynski, W.[Wojciech],
Hidden Evidential Markov Trees and Image Segmentation,
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Salzenstein, F., Collet, C., Petremand, M.,
Champs de Markov Flous pour Imagerie Multispectrale-Fuzzy Markov Random Fields for Multispectral Images,
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Derrode, S., Mercier, G., Pieczynski, W.,
Unsupervised multicomponent image segmentation combining a vectorial HMC Model and ICA,
ICIP03(II: 407-410).
IEEE DOI 0312
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Kudo, M., Yanagi, S., Shimbo, M.,
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PR(29), No. 4, April 1996, pp. 581-588.
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Jain, A.K.[Anil K.], and Flynn, P.J.[Patrick J.],
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Tanaka, E.,
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PAMI(16), No. 12, December 1994, pp. 1233-1238.
IEEE DOI BibRef 9412

Mukherjee, D.P., Pal, P., Das, J.,
Sonar Image Segmentation by Fuzzy C-Means,
SP(54), No. 3, November 1996, pp. 295-301. 9701
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Abrantes, A.J., Marques, J.S.,
Class of Constrained Clustering Algorithms for Object Boundary Extraction,
IP(5), No. 11, November 1996, pp. 1507-1521.
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Priebe, C.E.[Carey E.], Marchette, D.J., Rogers, G.W.,
Segmentation of Random-Fields via Borrowed Strength Density-Estimation,
PAMI(19), No. 5, May 1997, pp. 494-499.
IEEE DOI 9705
Segmentation using clustering of subregions. BibRef

Ceballos, J.C., Bottino, M.J.,
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Gupta, L., Sortrakul, T.,
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Kubota, T.[Toshiro], Huntsberger, T.L.[Terry L.],
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Kartikeyan, B., Sarkar, A., Majumder, K.L.,
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Bianchi, N., Bottoni, P., Mussio, P., Spinu, C., Garbay, C.,
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Bianchi, N., Bottoni, P., Spinu, C., Garbay, C., Mussio, P.,
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ICPR96(I: 228-232).
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Shen, X.Q., Spann, M., Nacken, P.,
Segmentation of 2D and 3D Images Through a Hierarchical Clustering Based on Region Modeling,
PR(31), No. 9, September 1998, pp. 1295-1309.
WWW Link. 9808
BibRef
Earlier:
Segmentation of 2D and 3D Images Through a Hierarchical Clustering Based on Region Modelling,
ICIP97(III: 50-53).
IEEE DOI BibRef

Shen, X.Q., Spann, M.,
3D Shape Modelling through a Constrained Estimation of a Bicubic B-spline Surface,
BMVC98(xx-yy). BibRef 9800
Earlier:
3D Shape Modelling Using a Multi-Scale Surface Model,
ICIP97(II: 478-481).
IEEE DOI BibRef

Mandal, D.P., Murthy, C.A., Pal, S.K.,
Analysis of IRS Imagery for Detecting Man-Made Objects with a Multivalued Recognition System,
SMC-A(26), No. 2, March 1996, pp. 241-247.
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Suri, J.S.[Jasjit S.], Haralick, R.M.[Robert M.], Sheehan, F.H.[Florence H.],
Greedy Algorithm for Error Correction in Automatically Produced Boundaries from Low Contrast Ventriculograms,
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Earlier:
Linear vs. Quadratic Optimization Algorithms for Bias Correction of Left Ventricle Chamber Boundaries in Low Contrast Projection Ventriculograms Produced from Xray Cardiac Catheterization Procedure,
CAIP99(108-117).
Springer DOI 9909
BibRef
Earlier:
Correction of Systematic Errors in Automatically Produced Boundaries from Low Contrast Ventriculograms,
ICPR96(IV: 361-365).
IEEE DOI 9608
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Suri, J.S.[Jasjit S.], Wu, D.[Dee], Reden, L.[Laura], Gao, J.B.[Jian-Bo], Singh, S.[Sameer], Laxminarayan, S.[Swamy],
Modeling Segmentation Via Geometric Deformable Regularizers, Pde And Level Sets In Still And Motion Imagery: A Revisit,
IJIG(1), No. 4, October 2001, pp. 681-734. 0110
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Rhouma, M.B.H.[Mohamed Ben Hadj], Frigui, H.[Hichem],
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PAMI(23), No. 2, February 2001, pp. 180-195.
IEEE DOI 0102
Clustering. Applied to segmentation, espeically to get the central in focus object from the background for database indexing. BibRef

Shimbo, M.[Masaru],
Fast Labelling of Natural Scenes Using Enhanced Knowledge,
PAA(4), No. 1, 2001, pp. 20-27.
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Fan, G.L.[Guo-Liang], Xia, X.G.[Xiang-Gen],
A joint multicontext and multiscale approach to Bayesian image segmentation,
GeoRS(39), No. 12, December 2001, pp. 2680-2688.
IEEE Top Reference. 0201
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And: Correction: GeoRS(40), No. 1, January 2002, pp. 229-229.
IEEE Top Reference. 0203
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Earlier:
Multiscale Texture Segmentation Using Hybrid Contextual Labeling Tree,
ICIP00(Vol III: 576-579).
IEEE DOI 0008
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Papin, C., Bouthemy, P., Rochard, G.,
Unsupervised segmentation of low clouds from infrared METEOSAT images based on a contextual spatio-temporal labeling approach,
GeoRS(40), No. 1, January 2002, pp. 104-114.
IEEE Top Reference. 0203
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Earlier:
Detection of low clouds in METEOSAT IR night-time images based on a contextual spatio-temporal labeling approach,
ICIP98(III: 561-565).
IEEE DOI 9810
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Sgrenzaroli, M., Baraldi, A., Eva, H., de Grandi, G., Achard, F.,
Contextual clustering for image labeling: an application to degraded forest assessment in Landsat TM images of the Brazilian Amazon,
GeoRS(40), No. 8, August 2002, pp. 1833-1848.
IEEE Top Reference. 0210
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Colombo, S.[Sergio], Chica-Olmo, M.[Mario], Abarca, F.[Francisco], Eva, H.[Hugh],
Variographic analysis of tropical forest cover from multi-scale remotely sensed imagery,
PandRS(58), No. 5-6, July 2004, pp. 330-341.
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Martínez, A.M.[Aleix M.], Mittrapiyanuruk, P.[Pradit], Kak, A.C.[Avinash C.],
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CVIU(95), No. 1, July 2004, pp. 72-85.
WWW Link. 0407
Alternative implementation of the k-way Ncut approach for image segmentation. Uses the clustering algorithm of Koontz and Fukunaga ( See also Application of the Karhunen-Loeve Expansion to Feature Selection and Ordering. ) which automatically chooses the number of clusters. BibRef

Farmer, M.E., Jain, A.K.,
A Wrapper-Based Approach to Image Segmentation and Classification,
IP(14), No. 12, December 2005, pp. 2060-2072.
IEEE DOI 0512
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Earlier: ICPR04(II: 106-109).
IEEE DOI 0409
BibRef

Farmer, M.E.[Michael Edward],
Image segmentation system and method,
US_Patent7,116,800, Oct 3, 2006
WWW Link. Isolating foreground object. BibRef 0610

Farmer, M.E.[Michael E.],
Application of the wrapper framework for image object detection,
ICPR08(1-4).
IEEE DOI 0812
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Lázaro, J.[Jesús], Arias, J.[Jagoba], Martín, J.L.[José L.], Zuloaga, A.[Aitzol], Cuadrado, C.[Carlos],
SOM Segmentation of gray scale images for optical recognition,
PRL(27), No. 16, December 2006, pp. 1991-1997.
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Thresholding; Clustering; Self organizing map BibRef

Pavan, M.[Massimiliano], Pelillo, M.[Marcello],
Dominant Sets and Pairwise Clustering,
PAMI(29), No. 1, January 2007, pp. 167-172.
IEEE DOI 0701
BibRef
Earlier:
Efficiently Segmenting Images with Dominant Sets,
ICIAR04(I: 17-24).
Springer DOI 0409
BibRef
Earlier:
Dominant sets and hierarchical clustering,
ICCV03(362-369).
IEEE DOI 0311
BibRef
And:
A new graph-theoretic approach to clustering and segmentation,
CVPR03(I: 145-152).
IEEE DOI 0307
BibRef

cluster based on dominant vertices in graph representation. See also Spatio-temporal Segmentation Using Dominant Sets.

Torsello, A.[Andrea], Pelillo, M.[Marcello],
Hierarchical Pairwise Segmentation Using Dominant Sets and Anisotropic Diffusion Kernels,
EMMCVPR09(182-192).
Springer DOI 0908
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Sperotto, A.[Anna], Pelillo, M.[Marcello],
Szemerédi's Regularity Lemma and Its Applications to Pairwise Clustering and Segmentation,
EMMCVPR07(13-27).
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Zoller, T.[Thomas], Buhmann, J.M.[Joachim M.],
Robust Image Segmentation Using Resampling and Shape Constraints,
PAMI(29), No. 7, July 2007, pp. 1147-1164.
IEEE DOI 0706
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Earlier:
Shape constrained image segmentation by parametric distributional clustering,
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IEEE DOI 0408
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Hermes, L.[Lothar], Zöller, T.[Thomas], Buhmann, J.M.[Joachim M.],
Parametric Distributional Clustering for Image Segmentation,
ECCV02(III: 577 ff.).
Springer DOI 0205
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Hermes, L., Buhmann, J.M.,
Contextual Classification by Entropy-Based Polygonization,
CVPR01(II:442-447).
IEEE DOI 0110
Use context in pixel classification. Allow polygonal boundaries rather than just smoothing. BibRef

Chang, H.[Hong], Yeung, D.Y.[Dit-Yan],
Robust path-based spectral clustering,
PR(41), No. 1, January 2008, pp. 191-203.
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Robust Path-Based Spectral Clustering with Application to Image Segmentation,
ICCV05(I: 278-285).
IEEE DOI 0510
Path-based clustering; Spectral clustering; Robust statistics; Unsupervised learning; Semi-supervised learning; Image segmentation BibRef

Dam, E.B., Loog, M.[Marco],
Efficient Segmentation by Sparse Pixel Classification,
MedImg(27), No. 10, October 2008, pp. 1525-1534.
IEEE DOI 0810
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Wang, Z.M.[Zhi Min], Soh, Y.C.[Yeng Chai], Song, Q.[Qing], Sim, K.[Kang],
Adaptive spatial information-theoretic clustering for image segmentation,
PR(42), No. 9, September 2009, pp. 2029-2044.
Elsevier DOI 0905
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Earlier: A1, A3, A2, A4:
Image clustering by incorporating adaptive spatial connectivity,
ICARCV08(657-661).
IEEE DOI 1109
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And: A1, A3, A2, A4:
Improved Adaptive Spatial Information Clustering for Image Segmentation,
ISVC08(I: 308-317).
Springer DOI 0812
Spatial clustering; Image segmentation; Information-theoretic approach BibRef

Wang, Z.M.[Zhi-Min], Song, Q.[Qing], Soh, Y.C.[Yeng Chai], Sim, K.[Kang],
An adaptive spatial information-theoretic fuzzy clustering algorithm for image segmentation,
CVIU(117), No. 10, 2013, pp. 1412-1420.
Elsevier DOI 1309
Image segmentation BibRef

Sfikas, G.[Giorgos], Nikou, C.[Christophoros], Galatsanos, N.[Nikolaos], Heinrich, C.[Christian],
Spatially Varying Mixtures Incorporating Line Processes for Image Segmentation,
JMIV(36), No. 2, February 2010, pp. xx-yy.
Springer DOI 1002
BibRef
Earlier: A1, A2, A3, Only:
Edge preserving spatially varying mixtures for image segmentation,
CVPR08(1-7).
IEEE DOI 0806
BibRef
Earlier:
Robust Image Segmentation with Mixtures of Student's t-Distributions,
ICIP07(I: 273-276).
IEEE DOI 0709
BibRef

Sfikas, G.[Giorgos], Heinrich, C.[Christian], Nikou, C.[Christophoros],
Multiple Atlas Inference and Population Analysis Using Spectral Clustering,
ICPR10(2500-2503).
IEEE DOI 1008
BibRef

Sfikas, G.[Giorgos], Nikou, C.[Christophoros], Galatsanos, N.[Nikolaos], Heinrich, C.[Christian],
Majorization-minimization mixture model determination in image segmentation,
CVPR11(2169-2176).
IEEE DOI 1106
BibRef

Sfikas, G.[Giorgos], Heinrich, C.[Christian], Zallat, J.[Jihad], Nikou, C.[Christophoros], Galatsanos, N.[Nikos],
Recovery of polarimetric Stokes images by spatial mixture models,
JOSA-A(28), No. 3, March 2011, pp. 465-474.
WWW Link. 1103
BibRef
Earlier:
Joint recovery and segmentation of polarimetric images using a compound MRF and mixture modeling,
ICIP09(3901-3904).
IEEE DOI 0911
BibRef

Nikou, C.[Christophoros], Likas, A., Galatsanos, N.[Nikolaos],
A Bayesian Framework for Image Segmentation With Spatially Varying Mixtures,
IP(19), No. 9, September 2010, pp. 2278-2289.
IEEE DOI 1008
See also Tomographic Image Reconstruction with a Spatially Varying Gaussian Mixture Prior. BibRef

Tung, F.[Frederick], Wong, A.[Alexander], Clausi, D.A.[David A.],
Enabling scalable spectral clustering for image segmentation,
PR(43), No. 12, December 2010, pp. 4069-4076.
Elsevier DOI 1003
Spectral clustering; Image segmentation; Stochastic ensemble consensus BibRef

Wong, A.[Alexander], Wang, X.Y.[Xiao Yu],
Monte Carlo cluster refinement for noise robust image segmentation,
JVCIR(23), No. 7, October 2012, pp. 984-994.
Elsevier DOI 1209
Monte Carlo; Clustering; Image; Noise robust; Segmentation; Stochastic; Local spatial-feature context; Maximum a posterior BibRef

Wang, X.Y.[Xiang-Yang], Wang, T.[Ting], Bu, J.[Juan],
Color image segmentation using pixel wise support vector machine classification,
PR(44), No. 4, April 2011, pp. 777-787.
Elsevier DOI 1101
Image segmentation; Support vector machine; Fuzzy c-means; Local homogeneity model; Gabor filter BibRef

Saha, S.[Sriparna], Maulik, U.[Ujjwal],
A New Line Symmetry Distance Based Automatic Clustering Technique: Application To Image Segmentation,
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Chen, L.[Long], Chen, C.L.P., Lu, M.[Mingzhu],
A Multiple-Kernel Fuzzy C-Means Algorithm for Image Segmentation,
SMC-B(41), No. 5, October 2011, pp. 1263-1274.
IEEE DOI 1110
BibRef

Nguyen, T.M.[Thanh Minh], Wu, Q.M.J.[Q.M. Jonathan],
Dirichlet Gaussian mixture model: Application to image segmentation,
IVC(29), No. 12, November 2011, pp. 818-828.
Elsevier DOI 1112
BibRef
Earlier:
A Fuzzy C-Means Based Spatial Pixel and Membership Relationships for Image Segmentation,
CRV11(278-284).
IEEE DOI 1105
BibRef
Earlier:
Maximum likelihood neural network based on the correlation among neighboring pixels for noisy image segmentation,
ICIP08(3020-3023).
IEEE DOI 0810
extended from Gaussian mixture model. Dirichlet Gaussian mixture model; Dirichlet distribution; Spatial constraints; Gradient method; Image segmentation BibRef

Zhang, H.[Hui], Wu, Q.M.J.[Q. M. Jonathan], Nguyen, T.M.[Thanh Minh],
Image segmentation by a new weighted student's t-mixture model,
IET-PR(7), No. 3, 2013, pp. -.
DOI Link 1307
BibRef
And:
Image segmentation by a robust generalized fuzzy c-means algorithm,
ICIP13(4024-4028)
IEEE DOI 1402
BibRef
Earlier:
Bayesian feature selection and model detection for student's t-mixture distributions,
ICPR12(1631-1634).
WWW Link. 1302
Fuzzy C-Means; Generalized Mean; bImage segmentation; Spatial constraints BibRef

Nguyen, T.M.[Thanh Minh], Wu, Q.M.J.[Q.M. Jonathan], Zhang, H.[Hui],
Bounded generalized Gaussian mixture model,
PR(47), No. 9, 2014, pp. 3132-3142.
Elsevier DOI 1406
Mixture model BibRef

Nguyen, T.M.[Thanh Minh], Wu, Q.M.J.[Q. M. Jonathan],
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IEEE DOI 1312
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IEEE DOI 1110
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Liu, X.B.[Xiao-Bai], Xu, Q.[Qian], Ma, J.Y.[Jia-Yi], Jin, H.[Hai], Zhang, Y.D.[Yan-Duo],
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IEEE DOI 1405
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Ichimura, N.,
Inexhaustive region segmentation by robust clustering,
ICIP95(III: 77-80).
IEEE DOI 9510
BibRef

Zhou, J.[Jing], Fang, X.[Xiang], Ghosh, B.J.,
Image segmentation based on multiresolution filtering,
ICIP94(III: 483-487).
IEEE DOI 9411
BibRef

Herlin, I.L., Nguyen, C., Graffigne, C.,
Stochastic Segmentation of Ultrasound Images,
ICPR92(I:289-292).
IEEE DOI BibRef 9200

Fassnacht, C., Devijver, P.A.,
Image Segmentation With A Propagator Markov Mesh Model,
ICPR94(A:510-513).
IEEE DOI BibRef 9400

Bruynooghe, M.,
A very efficient strategy for very large data sets clustering: application to image segmentation,
ICPR88(I: 623-627).
IEEE DOI 8811
BibRef

Zucker, S.W., Leclerc, Y.G.,
Intensity Clustering by Relaxation,
PRAI-78(192-197). BibRef 7800

Chapter on 2-D Region Segmentation Techniques, Snakes, Active Contours continues in
Unsupervised Clustering and Optimal Clusters for Segmentation .


Last update:Jun 22, 2017 at 17:22:14