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],
Image segmentation using a hierarchical student's-t mixture model,
IET-IPR(11), No. 11, November 2017, pp. 1094-1102.
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PR(47), No. 9, 2014, pp. 3132-3142.
Elsevier DOI 1406
Mixture model BibRef

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Cyber(43), No. 2, April 2013, pp. 751-765.
IEEE DOI 1303
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A robust non-symmetric mixture models for image segmentation,
ICIP12(273-276).
IEEE DOI 1302
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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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CRV13(332-339)
IEEE DOI 1308
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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,
MedImg(31), No. 1, January 2012, pp. 103-116.
IEEE DOI 1201

See also Online Adaptive Fuzzy Clustering and Its Application for Background Suppression, An. BibRef

Nguyen, T.M.[Thanh Minh], Wu, Q.M.J.[Q.M. Jonathan],
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IP(23), No. 3, March 2014, pp. 1210-1225.
IEEE DOI 1403
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)
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Elsevier DOI 1503
Bounded data BibRef

Zhang, H.[Hui], Wu, Q.M.J.[Q.M. Jonathan], Nguyen, T.M.[Thanh Minh],
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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.
DOI Link 1411
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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,
GeoRS(52), No. 7, July 2014, pp. 4391-4403.
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,
IET-IPR(6), No. 1, 2012, pp. 1-10.
DOI Link 1202
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Kim, S., Kang, M.,
Multiple-Region Segmentation Without Supervision by Adaptive Global Maximum Clustering,
IP(21), No. 4, April 2012, pp. 1600-1612.
IEEE DOI 1204
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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
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Earlier:
Genetic algorithm clustering for color image quantization,
EUVIP11(83-87).
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],
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,
IP(23), No. 7, July 2014, pp. 3057-3070.
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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PAMI(40), No. 8, August 2018, pp. 2023-2029.
IEEE DOI 1807
Approximation algorithms, Computational complexity, Finite impulse response filters, Optimization, probabilistic querying BibRef

Mostajabi, M.[Mohammadreza], Gholampour, I.[Iman],
A robust multilevel segment description for multi-class object recognition,
MVA(26), No. 1, January 2015, pp. 15-30.
Springer DOI 1503
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Li, S., Wu, D.O.,
Modularity-Based Image Segmentation,
CirSysVideo(25), No. 4, April 2015, pp. 570-581.
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,
IET-CV(7), No. 5, October 2013, pp. 354-361.
DOI Link 1402
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,
GbRPR15(262-271).
Springer DOI 1511
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Hou, J.[Jian], Xia, Q.[Qi], Qi, N.M.[Nai-Ming],
Experimental study on dominant sets clustering,
IET-CV(9), No. 2, 2015, pp. 208-215.
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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],
Incorporating Adaptive Local Information Into Fuzzy Clustering for Image Segmentation,
IP(24), No. 11, November 2015, pp. 3990-4000.
IEEE DOI 1509
fuzzy set theory BibRef

Xiong, T.S.[Tai-Song], Zhang, L.[Lei], Yi, Z.[Zhang],
Double Gaussian mixture model for image segmentation with spatial relationships,
JVCIR(34), No. 1, 2016, pp. 135-145.
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
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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,
JVCIR(40, Part A), No. 1, 2016, pp. 345-356.
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],
Towards parameter-independent data clustering and image segmentation,
PR(60), No. 1, 2016, pp. 25-36.
Elsevier DOI 1609
Dominant sets BibRef

Chang, D.X.[Dong-Xia], Zhao, Y.[Yao], Liu, L.[Lian], Zheng, C.W.[Chang-Wen],
A dynamic niching clustering algorithm based on individual-connectedness and its application to color image segmentation,
PR(60), No. 1, 2016, pp. 334-347.
Elsevier DOI 1609
Clustering BibRef

Gharieb, R.R., Gendy, G., Abdelfattah, A.,
C-means clustering fuzzified by two membership relative entropy functions approach incorporating local data information for noisy image segmentation,
SIViP(11), No. 3, March 2017, pp. 541-548.
WWW Link. 1702
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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],
Fuzzy model-based clustering and its application in image segmentation,
PR(68), No. 1, 2017, pp. 141-157.
Elsevier DOI 1704
Image segmentation BibRef

Zheng, J.[Jia], Zhang, D.H.[Ding-Hua], Huang, K.D.[Kui-Dong], Sun, Y.X.[Yuan-Xi],
Adaptive image segmentation method based on the fuzzy c-means with spatial information,
IET-IPR(12), No. 5, May 2018, pp. 785-792.
DOI Link 1804
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Zheng, J.[Jia], Zhang, D.H.[Ding-Hua], Huang, K.D.[Kui-Dong], Sun, Y.X.[Yuan-Xi],
Image segmentation framework based on optimal multi-method fusion,
IET-IPR(13), No. 1, January 2019, pp. 186-195.
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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

Xu, Y.[Yan], Chen, R.Z.[Rui-Zhi], Li, Y.[Yu], Zhang, P.[Peng], Yang, J.[Jie], Zhao, X.M.[Xue-Mei], Liu, M.Y.[Meng-Yun], Wu, D.W.[De-Wen],
Multispectral Image Segmentation Based on a Fuzzy Clustering Algorithm Combined with Tsallis Entropy and a Gaussian Mixture Model,
RS(11), No. 23, 2019, pp. xx-yy.
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Wen, J.Y.[Jin-Yu], Xuan, S.B.[Shi-Bin], Li, Y.Q.[Yu-Qi], Peng, Q.H.[Qi-Hui], Gao, Q.[Qing],
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IET-IPR(14), No. 3, 28 February 2020, pp. 576-584.
DOI Link 2002
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Selwyn, E.J.[Ebenezer Juliet], Velayutham, S.S.[Selvi Shunmuga], George, J.F.D.[Jemi Florinabel Deva],
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IET-IPR(14), No. 8, 19 June 2020, pp. 1605-1613.
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Li, S.C.[Song-Cheng], Lu, J.Y.[Jun-Yong], Cheng, L.[Long], Li, X.P.[Xiang-Ping],
Novel local information kernelized fuzzy C-means algorithm for image segmentation,
IJIST(31), No. 2, 2021, pp. 786-801.
DOI Link 2105
contextual information, fuzzy C-means algorithm, image segmentation, novel dissimilarity metric BibRef

Zhang, H.[Hang], Li, H.[Haili], Chen, N.[Ning], Chen, S.F.[Sheng-Feng], Liu, J.[Jian],
Novel fuzzy clustering algorithm with variable multi-pixel fitting spatial information for image segmentation,
PR(121), 2022, pp. 108201.
Elsevier DOI 2109
Fuzzy clustering, Image segmentation, Spatial information, Variable filter window, Variable generalized neighbourhood window BibRef

Liu, R.C.[Ruo-Chen], Xue, M.L.[Min-Lei], Lv, H.Y.[Hao-Yuan],
Adaptive Feature Weights Based Double-Layer Multi-Objective Method for SAR Image Segmentation,
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Wu, C.M.[Cheng-Mao], Zhang, X.[Xue],
Total Bregman divergence-driven possibilistic fuzzy clustering with kernel metric and local information for grayscale image segmentation,
PR(128), 2022, pp. 108686.
Elsevier DOI 2205
Image segmentation, Fuzzy clustering, Total Bregman divergence, Polynomial kernel function, Possibilistic typicality BibRef

Yang, Z.[Zenan], Niu, H.P.[Hai-Peng], Wang, X.X.[Xiao-Xuan], Fan, L.X.[Liang-Xin],
A segmentation method based on the deep fuzzy segmentation model in combined with SCANDLE clustering,
PR(146), 2024, pp. 110027.
Elsevier DOI 2311
Fuzzy clustering segmentation algorithm, Deep fuzzy segmentation model, SCANDLE, Matrix construction algorithm BibRef

Zhang, D.M.[Da-Ming], Zhang, X.Y.[Xue-Yong], Liu, H.Y.[Hua-Yong],
Image segmentation by selecting eigenvectors based on extended information entropy,
IET-IPR(17), No. 13, 2023, pp. 3777-3788.
DOI Link 2311
clustering information, eigenvectors selection, image segmentation, spectral clustering BibRef

Roy, S.K.[Suman Kumar], Rudra, B.[Bhawana],
Quantum-inspired hybrid algorithm for image classification and segmentation: Q-Means++ max-cut method,
IJIST(34), No. 1, 2024, pp. e23015.
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image segmentaion, max-cut algorithm, Q-Means clustering, quantum annealing, quantum computing, tumor analysis BibRef


He, H.[Haodi], Yuan, Y.H.[Yu-Hui], Yue, X.Y.[Xiang-Yu], Hu, H.[Han],
RankSeg: Adaptive Pixel Classification with Image Category Ranking for Segmentation,
ECCV22(XXIX:682-700).
Springer DOI 2211
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Shi, H.C.[Heng-Can], Li, H.L.[Hong-Liang], Wu, Q.B.[Qing-Bo], Song, Z.C.[Zi-Chen],
Scene Parsing via Integrated Classification Model and Variance-Based Regularization,
CVPR19(5302-5311).
IEEE DOI 2002
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Fehri, A., Velasco-Forero, S., Meyer, F.,
Characterizing Images by the Gromov-Hausdorff Distances Between Derived Hierarchies,
ICIP18(1213-1217)
IEEE DOI 1809
Image segmentation, Measurement, Shape, Support vector machines, Merging, Stochastic processes, Training, Gromov-Hausdorff Distance, Classification BibRef

Condat, L.[Laurent],
A Convex Approach to K-Means Clustering and Image Segmentation,
EMMCVPR17(220-234).
Springer DOI 1805
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Nadeem, S.A.[Syed Ahmed], Hoffman, E.A.[Eric A.], Saha, P.K.[Punam K.],
Path-Gradient: A Theory of Computing Full Intensity-Transition Between Two Points,
CIARP17(399-407).
Springer DOI 1802
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Fakhi, H., Bouattane, O., Youssfi, M., Hassan, O.,
New optimized GPU version of the k-means algorithm for large-sized image segmentation,
ISCV17(1-6)
IEEE DOI 1710
Clustering algorithms, Graphics processing units, Instruction sets, Kernel, GPGPU, K-means clustering, imageprocessing BibRef

Abdullah, S.M., Tischer, P., Wijewickrema, S., Paplinski, A.,
Parameter-free hierarchical image segmentation,
VCIP17(1-4)
IEEE DOI 1804
BibRef
Earlier:
Hierarchical Mutual Nearest Neighbour Image Segmentation,
DICTA16(1-8)
IEEE DOI 1701
data visualisation, image segmentation, trees (mathematics), hierarchical image segmentation algorithm. Clustering algorithms BibRef

Lobacheva, E., Veksler, O.[Olga], Boykov, Y.Y.[Yuri Y.],
Joint Optimization of Segmentation and Color Clustering,
ICCV15(1626-1634)
IEEE DOI 1602
Clustering algorithms BibRef

Hou, J.[Jian], Sha, C.S.[Chun-Shi], Cui, H.X.[Hong-Xia], Chi, L.[Lei],
Dominant Set Based Data Clustering and Image Segmentation,
MMMod16(I: 432-443).
Springer DOI 1601
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Sublime, J.[Jeremie], Troya-Galvis, A.[Andres], Bennani, Y.[Younes], Cornuejols, A.[Antoine], Gancarski, P.[Pierre],
Semantic rich ICM algorithm for VHR satellite images segmentation,
MVA15(45-48)
IEEE DOI 1507
Clustering algorithms BibRef

Shao, G.P.[Guang-Pu], Gao, J.B.[Jun-Bin], Wang, T.J.[Tian-Jiang], Liu, F.[Fang], Shu, Y.C.[Yu-Cheng], Yang, Y.[Yong],
Image Segmentation Based on Spatially Coherent Gaussian Mixture Model,
DICTA14(1-6)
IEEE DOI 1502
Gaussian processes BibRef

Escolano, F.[Francisco], Bonev, B.[Boyan], Hancock, E.R.[Edwin R.],
Quantum vs. Classical Ranking in Segment Grouping,
SSSPR14(203-212).
Springer DOI 1408
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Prakash, A., Balasubramanian, S., Raghunatha Sarma, R.,
Improvised eigenvector selection for spectral Clustering in image segmentation,
NCVPRIPG13(1-4)
IEEE DOI 1408
image segmentation BibRef

Huang, J.[Jing], You, S.[Suya],
Segmentation and matching: Towards a robust object detection system,
WACV14(325-332)
IEEE DOI 1406
Clustering algorithms BibRef

Zheng, F.H.[Fu-Hua], Zhang, C.M.[Cai-Ming], Zhang, X.F.[Xiao-Feng], Liu, Y.[Yi],
A fast anti-noise fuzzy C-means algorithm for image segmentation,
ICIP13(2728-2732)
IEEE DOI 1402
Image segmentation;fuzzy C-means;fuzzy clustering;spatial information BibRef

Bernard, G.[Guillaume], Verleysen, M.[Michel],
Segmentation with Incremental Classifiers,
CIAP13(II:81-90).
Springer DOI 1309
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Gallo, I.[Ignazio], Vanetti, M.[Marco], Albertini, S.[Simone], Nodari, A.[Angelo],
Multi-net System Configuration for Visual Object Segmentation by Error Backpropagation,
IbPRIA13(468-475).
Springer DOI 1307
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Gui, Y.[Yang], Bai, X.[Xiang], Li, Z.[Zheng], Yuan, Y.[Yun],
Color image segmentation using mean shift and improved spectral clustering,
ICARCV12(1386-1391).
IEEE DOI 1304
BibRef

Jang, D.[Daesik], Miller, G.[Gregor], Fels, S.[Sidney],
Transforming Cluster-Based Segmentation for Use in OpenVL by Mainstream Developers,
DevCen12(I:254-265).
Springer DOI 1304
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Scheuermann, B.[Björn], Schlosser, M.[Markus], Rosenhahn, B.[Bodo],
Efficient Pixel-Grouping Based on Dempster's Theory of Evidence for Image Segmentation,
ACCV12(I:745-759).
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Ayech, M.W.[Mohamed Walid], Ziou, D.[Djemel],
Ranked k-means clustering for terahertz image segmentation,
ICIP15(4391-4395)
IEEE DOI 1512
BibRef
Earlier:
Automated feature weighting and random pixel sampling in k-means clustering for terahertz image segmentation,
PBVS15(35-40)
IEEE DOI 1510
BibRef
Earlier:
Terahertz image segmentation based on K-harmonic-means clustering and statistical feature extraction modeling,
ICPR12(222-225).
WWW Link. 1302
Segmentation. Chemicals. BibRef

Cinbis, R.G.[Ramazan Gokberk], Sclaroff, S.[Stan],
Contextual Object Detection Using Set-Based Classification,
ECCV12(VI: 43-57).
Springer DOI 1210
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Yarkony, J.[Julian], Zhang, C.[Chong], Fowlkes, C.C.[Charless C.],
Hierarchical Planar Correlation Clustering for Cell Segmentation,
EMMCVPR15(492-504).
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Yarkony, J.[Julian], Ihler, A.[Alexander], Fowlkes, C.C.[Charless C.],
Fast Planar Correlation Clustering for Image Segmentation,
ECCV12(VI: 568-581).
Springer DOI 1210
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Glocker, B.[Ben], Pauly, O.[Olivier], Konukoglu, E.[Ender], Criminisi, A.[Antonio],
Joint Classification-Regression Forests for Spatially Structured Multi-Object Segmentation,
ECCV12(IV: 870-881).
Springer DOI 1210
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Kumar, M.P.[M. Pawan], Turki, H.[Haithem], Preston, D.[Dan], Koller, D.[Daphne],
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ICCV11(1800-1807).
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Contour-based joint clustering of multiple segmentations,
CVPR11(2385-2392).
IEEE DOI 1106
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Extracting spatially and spectrally coherent regions from multispectral images,
OTCBVS11(82-87).
IEEE DOI 1106
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Jiang, M.Y.[Ming-Yang], Li, C.X.[Chun-Xiao], Feng, J.F.[Ju-Fu], Wang, L.W.[Li-Wei],
Segmentation via NCuts and Lossy Minimum Description Length: A Unified Approach,
ACCV10(III: 213-224).
Springer DOI 1011
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Jiang, M.Y.[Ming-Yang], Li, C.X.[Chun-Xiao], Feng, J.F.[Ju-Fu], Wang, L.W.[Li-Wei],
Towards Hypothesis Testing and Lossy Minimum Description Length: A Unified Segmentation Framework,
ACCV10(III: 343-354).
Springer DOI 1011
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Sanchez, J.[Javier], Martinez, E.[Estibaliz], Arquero, A.[Agueda], Renza, D.[Diego],
Automatic Image Segmentation Optimized by Bilateral Filtering,
CIARP10(303-310).
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Image Segmentation for Robots: Fast Self-adapting Gaussian Mixture Model,
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A New Clustering Algorithm for Color Image Segmentation,
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Chen, Y.W.[Yen-Wei], Han, X.H.[Xian-Hua],
Supervised Local Subspace Learning for Region Segmentation and Categorization in High-Resolution Satellite Images,
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Abdelrahman, M.[Mostafa], El-Melegy, M.[Moumen], Farag, A.A.[Aly A.],
3D Object Classification Using Scale Invariant Heat Kernels with Collaborative Classification,
NORDIA12(I: 22-31).
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CRV12(153-160).
IEEE DOI 1207
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El-Melegy, M.[Moumen], Zanaty, E.A., Abd-Elhafiez, W.M.[Walaa M.], Farag, A.A.[Aly A.],
On Cluster Validity Indexes in Fuzzy and Hard Clustering Algorithms for Image Segmentation,
ICIP07(VI: 5-8).
IEEE DOI 0709
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Zhao, Y.J.[Yan-Jun], Wang, T.[Tao], Wang, P.[Peng], Hu, W.[Wei], Du, Y.Z.[Yang-Zhou], Zhang, Y.M.[Yi-Min], Xu, G.Y.[Guang-You],
Scene Segmentation and Categorization Using NCuts,
SLAM07(1-7).
IEEE DOI 0706
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Cleju, I.[Ioan], Fränti, P.[Pasi], Wu, X.L.[Xiao-Lin],
Clustering Based on Principal Curve,
SCIA05(872-881).
Springer DOI 0506
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Shental, N., Zomet, A., Hertz, T., Weiss, Y.,
Learning and Inferring Image Segmentations Using the GBP Typical Cut Algorithm,
ICCV03(1243-1250).
IEEE DOI 0311
Issues in clustering. BibRef

Wesolkowski, S.[Slawo], Fieguth, P.W.[Paul W.],
Hierarchical Region Mean-Based Image Segmentation,
CRV06(30-30).
IEEE DOI 0607
BibRef
Earlier:
Hierarchical Regions for Image Segmentation,
ICIAR04(I: 9-16).
Springer DOI 0409
BibRef
Earlier:
A probabilistic framework for image segmentation,
ICIP03(II: 451-454).
IEEE DOI 0312

See also Highlight and Shading Invariant Color Image Segmentation Using Simulated Annealing. BibRef

Legal-Ayala, H.A., Facon, J.,
Segmentation approach by learning: different image applications,
CIAP03(600-604).
IEEE DOI 0310
BibRef

Ren, X.F.[Xiao-Feng], Malik, J.,
Learning a classification model for segmentation,
ICCV03(10-17).
IEEE DOI 0311
BibRef

Singh, M.K., Ahuja, N.,
Mean-shift segmentation with wavelet-based bandwidth selection,
WACV02(43-47).
IEEE DOI 0303
BibRef

Mukherjee, D.P., Mohanta, P.P., Acton, S.T.,
Agglomerative clustering of feature data for image segmentation,
ICIP02(III: 269-272).
IEEE DOI 0210
BibRef
Earlier: A2, A1, A3:
Agglomerative clustering for image segmentation,
ICPR02(I: 664-667).
IEEE DOI 0211
BibRef

Romano, R., Vitulano, D.,
A Variational Representation for Efficient Noisy Segmentation,
WSCG02(POS-41).
HTML Version. 0209
BibRef

Aronsson, M.[Mattias], Borgefors, G.[Gunilla],
2D Segmentation and Labelling of Clustered Ring Shaped Objects,
SCIA01(P-W4A). 0206
BibRef

Keslassy, I., Kalman, M., Wang, D., Girod, B.,
Classification of Compound Images Based on Transform Coefficient Likelihood,
ICIP01(I: 750-753).
IEEE DOI 0108
BibRef

Pham, T.D.,
Image Segmentation Using Probabilistic Fuzzy C-means Clustering,
ICIP01(I: 722-725).
IEEE DOI 0108

See also Fuzzy posterior-probabilistic fusion. BibRef

Noordam, J.C., van den Broek, W.H.A.M., Buydens, L.M.C.,
Geometrically Guided Fuzzy C-means Clustering for Multivariate Image Segmentation,
ICPR00(Vol I: 462-465).
IEEE DOI 0009
BibRef

Venkatachalam, V.,
Image Classification Using Pseudo Power Signatures,
ICIP00(Vol I: 796-799).
IEEE DOI 0008
BibRef

Voles, P., Smith, A., Teal, M.,
Nautical Scene Segmentation using Variable Size Image Windows and Feature Space Reclustering,
ECCV00(II: 324-335).
Springer DOI 0003
BibRef

Schweitzer, H.[Haim],
Utilizing Scatter for Pixel Subspace Selection,
ICCV99(1111-1116).
IEEE DOI Use scatter matrix for clustering and indexing. BibRef 9900

Chardin, A.[Annabelle], Perez, P.[Patrick],
Mode of Posterior Marginals with Hierarchical Models,
ICIP99(I:324-328).
IEEE DOI BibRef 9900

Weiss, Y.[Yair],
Segmentation using Eigenvectors: A Unifying View,
ICCV99(975-982).
IEEE DOI BibRef 9900

Glasbey, C.A.,
Ultrasound Image Segmentation Using a Point Distribution Model in a Bayesian Framework,
BMVC96(Features, Segmentation). 9608
University of Edinburgh BibRef

Cortijo, F.J., de la Blanca, N.P.[N. Perez],
Automatic Estimation of the LVQ-1 Parameters: Applications to Multispectral Image Classification,
ICPR96(IV: 346-350).
IEEE DOI 9608
(Univ. de Granada, E) BibRef

Olk, J., Jonker, P.P.,
Bucket Processing: a Paradigm for Image Processing,
ICPR96(IV: 386-390).
IEEE DOI 9608
(Delft Univ. of Technology, NL) BibRef

Mari, M., Dellepiane, S.G.,
A Segmentation Method Based on Fuzzy Topology and Clustering,
ICPR96(II: 565-569).
IEEE DOI 9608
(Univ. di Genoa, I) BibRef

Umesh Adiga, U., Chaudhuri, B.B.,
Semi-Automatic Segmentation of Tissue Cells from Confocal Microscope Images,
ICPR96(III: 494-497).
IEEE DOI 9608
(Indian Statistical Institute, IND) BibRef

Gong, Y., Chuan, C., Guo, X.,
An Effective Color Image Segmentation Method for Handling Images under Uneven Illumination,
ICPR96(C82.1). 9608
(Nanyang Technological Univ., SGP) BibRef

Ido, S., Arai, S., Takamatsu, R., Sato, M.,
Stimulus-Driven Segmentation By Gaussian Functions,
ICPR96(II: 487-491).
IEEE DOI 9608
(Tokyo Inst. of Technology, J) BibRef

Dugelay, S., Augustin, J., Graffigne, C.,
Segmentation of Multibeam Acoustic Imagery in the Exploration of the Deep Sea-Bottom,
ICPR96(II: 437-446).
IEEE DOI 9608
(Ifremer Centre de Brest, F) BibRef

Atmaca, H.[Hamdi], Bulut, M., Demir, D.,
Histogram Based Fuzzy Kohonen Clustering Network for Image Segmentation,
ICIP96(II: 951-954).
IEEE DOI BibRef 9600

Ferryman, T.A., Bhanu, B.,
A Bayesian Approach for the Segmentation of SAR Images Using Dynamically Selected Neighborhoods,
ARPA96(891-896). BibRef 9600

Pudil, P.[Pavel], Novovicová, J.[Jana], Ferri, F.J.[Francesc J.], Kittler, J.V.[Josef V.],
Advances in the statistical methodology for the selection of image descriptors for visual pattern representation and classification,
CAIP95(832-837).
Springer DOI 9509
BibRef

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
Minimum Spanning Tree for Segmentation .


Last update:Mar 16, 2024 at 20:36:19