8.3.4 Classification Methods, Clustering for Region Segmentation

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

Haralick, R.M., and Kelly, G.,
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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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See also Maximum-Likelihood Parameter Estimation for Unsupervised Stochastic Model-Based Image Segmentation. BibRef

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Uchiyama, T., and Arbib, M.A.,
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Earlier:
Object Extraction System from a Color Image,
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Krishnapuram, R., and Freg, C.P.,
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Panda, D.P.,
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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.],
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CVGIP(22), No. 3, June 1983, pp. 313-326.
Elsevier DOI 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,
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Zhang, M.C., Haralick, R.M., Campbell, J.B.,
Multispectral Image Context Classification Using Stochastic Relaxation,
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Knapman, J.[John], Dickson, W.[Will],
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Zheng, Y.J.,
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Simpson, J.J., Keller, R.H.,
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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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Salzenstein, F., Pieczynski, W.,
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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,
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Salzenstein, F., Collet, C., Lecam, S., Hatt, M.,
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Elsevier DOI 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,
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Pieczynski, W.[Wojciech],
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Pieczynski, W.[Wojciech],
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Salzenstein, F., Collet, C., Petremand, M.,
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Kudo, M.[Mineichi], Yanagi, S.[Shinichi], Shimbo, M.[Masaru],
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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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Mukherjee, D.P., Pal, P., Das, J.,
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Abrantes, A.J., Marques, J.S.,
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Priebe, C.E.[Carey E.], Marchette, D.J., Rogers, G.W.,
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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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Bianchi, N., Bottoni, P., Spinu, C., Garbay, C., Mussio, P.,
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Shen, X.Q.[Xin-Quan], Spann, M.[Michael], Nacken, P.[Peter],
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Earlier:
Segmentation of 2D and 3D Images Through a Hierarchical Clustering Based on Region Modelling,
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Shen, X.Q., Spann, M.,
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3D Shape Modelling Using a Multi-Scale Surface Model,
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Mandal, D.P., Murthy, C.A., Pal, S.K.,
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Suri, J.S.[Jasjit S.], Haralick, R.M.[Robert M.], Sheehan, F.H.[Florence H.],
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Linear vs. Quadratic Optimization Algorithms for Bias Correction of Left Ventricle Chamber Boundaries in Low Contrast Projection Ventriculograms Produced from Xray Cardiac Catheterization Procedure,
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Springer DOI 9909
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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],
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Rhouma, M.B.H.[Mohamed Ben Hadj], Frigui, H.[Hichem],
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Clustering. Applied to segmentation, espeically to get the central in focus object from the background for database indexing. BibRef

Shimbo, M.[Masaru],
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Fan, G.L.[Guo-Liang], Xia, X.G.[Xiang-Gen],
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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).
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Papin, C., Bouthemy, P., Rochard, G.,
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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).
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Sgrenzaroli, M., Baraldi, A., Eva, H., de Grandi, G., Achard, F.,
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Colombo, S.[Sergio], Chica-Olmo, M.[Mario], Abarca, F.[Francisco], Eva, H.[Hugh],
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Elsevier DOI 0411
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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.
Elsevier DOI 0407
Alternative implementation of the k-way Ncut approach for image segmentation. Uses the clustering algorithm of Koontz and Fukunaga (
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Farmer, M.E., Jain, A.K.,
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IEEE DOI 0512
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Farmer, M.E.[Michael Edward],
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Farmer, M.E.[Michael E.],
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Lázaro, J.[Jesús], Arias, J.[Jagoba], Martín, J.L.[José L.], Zuloaga, A.[Aitzol], Cuadrado, C.[Carlos],
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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
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Earlier:
Efficiently Segmenting Images with Dominant Sets,
ICIAR04(I: 17-24).
Springer DOI 0409
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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,
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Zoller, T.[Thomas], Buhmann, J.M.[Joachim M.],
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IEEE DOI 0408
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Hermes, L.[Lothar], Zöller, T.[Thomas], Buhmann, J.M.[Joachim M.],
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Hermes, L., Buhmann, J.M.,
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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.
Elsevier DOI 0710
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Robust Path-Based Spectral Clustering with Application to Image Segmentation,
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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,
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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,
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Image clustering by incorporating adaptive spatial connectivity,
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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
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Earlier: A1, A2, A3, Only:
Edge preserving spatially varying mixtures for image segmentation,
CVPR08(1-7).
IEEE DOI 0806
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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
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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.
See also Tomographic Image Reconstruction with a Spatially Varying Gamma 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,
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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.Z.[Ming-Zhu],
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-IPR(7), No. 3, 2013, pp. 240-251.
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

Kong, L.C.[Ling-Cheng], Zhang, H.[Hui], Zheng, Y.H.[Yu-Hui], Chen, Y.J.[Yun-Jie], Zhu, J.Z.[Jie-Zhong], Wu, Q.M.M.J.[Qing-Ming M. Jonathan],
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Elsevier DOI 1406
Mixture model BibRef

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Cyber(43), No. 2, April 2013, pp. 751-765.
IEEE DOI 1303
BibRef
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A robust non-symmetric mixture models for image segmentation,
ICIP12(273-276).
IEEE DOI 1302
BibRef

Nguyen, T.M.[Thanh Minh], Wu, Q.M.J.[Q.M. Jonathan],
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CirSysVideo(23), No. 4, April 2013, pp. 621-635.
IEEE DOI 1304
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A Dynamic Bayesian Framework for Motion Segmentation,
CRV13(332-339)
IEEE DOI 1308
Bayes methods BibRef

Nguyen, T.M.[Thanh Minh], Wu, Q.M.J.[Q.M. Jonathan],
Robust Student's-t Mixture Model With Spatial Constraints and Its Application in Medical Image Segmentation,
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IEEE DOI 1201

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Nguyen, T.M.[Thanh Minh], Wu, Q.M.J.[Q.M. Jonathan],
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feature extraction BibRef

Nguyen, T.M.[Thanh Minh], Wu, Q.M.J.[Q. M. Jonathan],
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Clustering algorithms BibRef

Nguyen, T.M.[Thanh Minh], Wu, Q.M.J.[Q.M. Jonathan], Mukherjee, D.[Dibyendu],
Feature Ranking in Dynamic Texture Clustering,
CRV15(109-116)
IEEE DOI 1507
Clustering algorithms BibRef

Nguyen, T.M.[Thanh Minh], Wu, Q.M.J.[Q.M. Jonathan],
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PR(48), No. 6, 2015, pp. 2084-2095.
Elsevier DOI 1503
Bounded data BibRef

Zhang, H.[Hui], Wu, Q.M.J.[Q.M. Jonathan], Nguyen, T.M.[Thanh Minh],
Image segmentation by dirichlet process mixture model with generalised mean,
IET-IPR(8), No. 2, February 2014, pp. 103-111.
DOI Link 1403
hidden Markov models BibRef

Zhang, H.[Hui], Wu, Q.M.J.[Q.M. Jonathan], Zheng, Y.H.[Yu-Hui], Nguyen, T.M.[Thanh Minh], Wang, D.C.[Ding-Cheng],
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IET-IPR(8), No. 10, October 2014, pp. 571-581.
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Zhang, H.[Hui], Wu, Q.M.J., Nguyen, T.M.[Thanh Minh],
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IEEE DOI 1302
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Zhang, H.[Hui], Wu, Q.M.J., Nguyen, T.M.[Thanh Minh], Sun, X.,
Synthetic Aperture Radar Image Segmentation by Modified Student's t-Mixture Model,
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IEEE DOI 1403
Approximation methods BibRef

Mukherjee, D.[Dibyendu], Wu, Q.M.J.[Q.M. Jonathan], Nguyen, T.M.[Thanh Minh],
Multiresolution Based Gaussian Mixture Model for Background Suppression,
IP(22), No. 12, 2013, pp. 5022-5035.
IEEE DOI 1312
BibRef
Earlier:
Bilateral filter based mixture model for image segmentation,
ICIP12(281-284).
IEEE DOI 1302
Gaussian processes BibRef

Bong, C.W., Rajeswari, M.,
Multiobjective clustering with metaheuristic: Current trends and methods in image segmentation,
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DOI Link 1202
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Kim, S., Kang, M.,
Multiple-Region Segmentation Without Supervision by Adaptive Global Maximum Clustering,
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IEEE DOI 1204
BibRef

Wang, L.J.[Li-Jun], Dong, M.[Ming],
Multi-level Low-rank Approximation-based Spectral Clustering for image segmentation,
PRL(33), No. 16, 1 December 2012, pp. 2206-2215.
Elsevier DOI 1210
Image segmentation; Matrix approximation; Spectral clustering BibRef

Szilágyi, L.[László],
Lessons to learn from a mistaken optimization,
PRL(36), No. 1, 2014, pp. 29-35.
Elsevier DOI 1312
Image segmentation BibRef

Ducournau, A.[Aurélien], Bretto, A.[Alain],
Random walks in directed hypergraphs and application to semi-supervised image segmentation,
CVIU(120), No. 1, 2014, pp. 91-102.
Elsevier DOI 1403
Directed hypergraph BibRef

Bretto, A.[Alain], Ducournau, A.[Aurélien], Rital, S.[Soufiane],
A Hypergraph Reduction Algorithm for Joint Segmentation and Classification of Satellite Image Content,
CIARP10(38-45).
Springer DOI 1011

See also Hypergraph Imaging: An Overview. BibRef

Bellala Belahbib, F.Z.[Fatima Zohra], Souami, F.[Feryel],
A genetic algorithm-based clustering and two-scan labelling for colour image segmentation,
IJCVR(4), No. 1-2, 2014, pp. 86-98.
DOI Link 1403
BibRef
Earlier:
Genetic algorithm clustering for color image quantization,
EUVIP11(83-87).
IEEE DOI 1110
BibRef

Liu, X.B.[Xiao-Bai], Xu, Q.[Qian], Ma, J.Y.[Jia-Yi], Jin, H.[Hai], Zhang, Y.D.[Yan-Duo],
MsLRR: A Unified Multiscale Low-Rank Representation for Image Segmentation,
IP(23), No. 5, May 2014, pp. 2159-2167.
IEEE DOI 1405
Feature extraction
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Sourati, J.[Jamshid], Erdogmus, D.[Deniz], Dy, J.G.[Jennifer G.], Brooks, D.H.,
Accelerated Learning-Based Interactive Image Segmentation Using Pairwise Constraints,
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IEEE DOI 1407
Clustering algorithms BibRef

Sourati, J.[Jamshid], Akcakaya, M.[Murat], Erdogmus, D.[Deniz], Leen, T.K.[Todd K.], Dy, J.G.[Jennifer G.],
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IEEE DOI 1807
Approximation algorithms, Computational complexity, Finite impulse response filters, Optimization, probabilistic querying BibRef

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Li, S., Wu, D.O.,
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IEEE DOI 1504
Clustering algorithms BibRef

Hou, J.[Jian], Xu, E., Liu, W.X.[Wei-Xue], Xia, Q.[Qi], Qi, N.M.[Nai-Ming],
A density-based enhancement to dominant sets clustering,
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graph theory. Oversegmentation. Sensitivity to distance measure. BibRef

Hou, J.[Jian], Sha, C.S.[Chun-Shi], E, X.[Xu], Xia, Q.[Qi], Qi, N.M.[Nai-Ming],
Density Based Cluster Extension and Dominant Sets Clustering,
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Hou, J.[Jian], Xia, Q.[Qi], Qi, N.M.[Nai-Ming],
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game theory BibRef

Hou, J.[Jian], Sha, C.[Chunshi], Chi, L.[Lei], Xia, Q.[Qi], Qi, N.M.[Nai-Ming],
Merging dominant sets and DBSCAN for robust clustering and image segmentation,
ICIP14(4422-4426)
IEEE DOI 1502
Clustering algorithms BibRef

Liu, G.Y.[Guo-Ying], Zhang, Y.[Yun], Wang, A.[Aimin],
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IEEE DOI 1509
fuzzy set theory BibRef

Xiong, T.S.[Tai-Song], Zhang, L.[Lei], Yi, Z.[Zhang],
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Elsevier DOI 1601
Markov random model BibRef

Parsi, A.[Ashkan], Sorkhi, A.G.[Ali Ghanbari], Zahedi, M.[Morteza],
Improving the unsupervised LBG clustering algorithm performance in image segmentation using principal component analysis,
SIViP(10), No. 2, February 2016, pp. 301-309.
Springer DOI 1601
BibRef

Hou, J., Gao, H., Li, X.,
DSets-DBSCAN: A Parameter-Free Clustering Algorithm,
IP(25), No. 7, July 2016, pp. 3182-3193.
IEEE DOI 1606
image segmentation BibRef

Zhou, D., Zhou, H.,
Minimisation of local within-class variance for image segmentation,
IET-IPR(10), No. 8, 2016, pp. 608-615.
DOI Link 1608
image segmentation BibRef

Xiong, T.S.[Tai-Song], Huang, Y.Y.[Yuan-Yuan], Gou, J.P.[Jian-Ping], Hu, J.R.[Jin-Rong],
A unified Bayesian mixture model framework via spatial information for grayscale image segmentation,
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Elsevier DOI 1609
Spatially variant finite mixture model BibRef

Hou, J.[Jian], Liu, W.X.[Wei-Xue], E, X.[Xu], Cui, H.X.[Hong-Xia],
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Elsevier DOI 1609
Dominant sets BibRef

Chang, D.X.[Dong-Xia], Zhao, Y.[Yao], Liu, L.[Lian], Zheng, C.W.[Chang-Wen],
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Clustering BibRef

Gharieb, R.R., Gendy, G., Abdelfattah, A.,
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Choy, S.K.[Siu Kai], Lam, S.Y.[Shu Yan], Yu, K.W.[Kwok Wai], Lee, W.Y.[Wing Yan], Leung, K.T.[King Tai],
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Image segmentation BibRef

Zheng, J.[Jia], Zhang, D.H.[Ding-Hua], Huang, K.D.[Kui-Dong], Sun, Y.X.[Yuan-Xi],
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Zheng, J.[Jia], Zhang, D.H.[Ding-Hua], Huang, K.D.[Kui-Dong], Sun, Y.X.[Yuan-Xi],
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Luo, M., Yan, C., Zheng, Q., Chang, X., Chen, L., Nie, F.,
Discrete Multi-Graph Clustering,
IP(28), No. 9, Sep. 2019, pp. 4701-4712.
IEEE DOI 1908
computational complexity, graph theory, image segmentation, optimisation, pattern clustering, image segmentation BibRef