8.3.4.2 Unsupervised Clustering and Optimal Clusters for Segmentation

Darling, Jr., E.M.[Eugene M.], Raudseps, J.G.[Juris G.],
Non-parametric unsupervised learning with applications to image classification,
PR(2), No. 4, December 1970, pp. 313-335.
Elsevier DOI 0309
BibRef

Rajasekaran, P.K., Srinath, M.D.,
Unsupervised learning in nongaussian pattern recognition,
PR(4), No. 4, December 1972, pp. 401-416.
Elsevier DOI 0309
BibRef

Gitman, I.[Israel],
A parameter-free clustering model,
PR(4), No. 3, October 1972, pp. 307-315.
Elsevier DOI 0309
BibRef

Coleman, G., and Andrews, H.C.,
Image Segmentation and Clustering,
PIEEE(67), No. 5, May 1979, pp. 773-785. BibRef 7905
Earlier:
A Bottom Up Image Segmentor,
DARPA77(44-54). Clustering. Find the optimal number of clusters along with the best clusters (try all numbers of clusters until a criterion decreases). BibRef

Andrews, H.C.,
Analytic Results of the Coleman Segmentor,
DARPAO77(96-103). BibRef 7700

Gowda, K.C.[K. Chidananda],
A Feature Reduction and Unsupervised Classification Algorithm for Multispectral Data,
PR(17), No. 6, 1984, pp. 667-676.
Elsevier DOI BibRef 8400

Veijanen, A.[Ari],
Unsupervised Image Segmentation Using An Unlabeled Region Process,
PR(27), No. 6, June 1994, pp. 841-852.
Elsevier DOI BibRef 9406

Peng, A., Pieczynski, W.[Wojciech],
Adaptive Mixture Estimation and Unsupervised Local Bayesian Image Segmentation,
GMIP(57), No. 5, September 1995, pp. 389-399. BibRef 9509

Caillol, H., Pieczynski, W., Hillion, A.,
Estimation of Fuzzy Gaussian Mixture and Unsupervised Statistical Image Segmentation,
IP(6), No. 3, March 1997, pp. 425-440.
IEEE DOI 9703

See also Estimation of Generalized Mixtures and Its Application in Image Segmentation. BibRef

Braathen, B., Pieczynski, W., Masson, P.,
Global and Local Methods of Unsupervised Bayesian Segmentation of Images,
MGV(2), No. 1, 1993, pp. 39-52. BibRef 9300

Masson, P., Pieczynski, W.,
SEM Algorithm and Unsupervised Segmentation of Satellite Images,
GeoRS(31), No. 3, 1993, pp. 618-633. BibRef 9300

Benmiloud, B., Pieczynski, W.,
Estimation des Parametres dans les Chaines de Markov Cachees et Segmentation d'Images,
Traitement du Signal(12), No. 5, 1995, pp. 433-454. BibRef 9500

Giordana, N.[Nathalie], Pieczynski, W.[Wojciech],
Estimation of Generalized Multisensor Hidden Markov-Chains and Unsupervised Image Segmentation,
PAMI(19), No. 5, May 1997, pp. 465-475.
IEEE DOI 9705
BibRef
Earlier:
Unsupervised Segmentation of Multisensor Images Using Generalized Hidden Markov Chains,
ICIP96(III: 987-990).
IEEE DOI 4-Class segmentation examples. Markov chains, not Markov Fields. And Mixture esitmation to switch from one to another. BibRef

Boudaren, M.E.[M. El_Yazid], Monfrini, E., Pieczynski, W.,
Unsupervised Segmentation of Random Discrete Data Hidden With Switching Noise Distributions,
SPLetters(19), No. 10, October 2012, pp. 619-622.
IEEE DOI 1209
BibRef

Habbouchi, A., Boudaren, M.E.[M. El_Yazid], Aïssani, A., Pieczynski, W.,
Unsupervised Segmentation of Markov Random Fields Corrupted by Nonstationary Noise,
SPLetters(23), No. 11, November 2016, pp. 1607-1611.
IEEE DOI 1609
Gaussian processes BibRef

Giordana, N.[Nathalie],
Segmentation non Supervisee d'Images Multispectrales par Chaines de Markov Cachees,
Ph.D.Thesis, Univ. de Tech. de Compiegne, 1996. BibRef 9600

Bensaid, A.M.[Amine M.], Hall, L.O.[Lawrence O.], Bezdek, J.C.[James C.], Clarke, L.P.[Laurence P.],
Partially Supervised Clustering for Image Segmentation,
PR(29), No. 5, May 1996, pp. 859-871.
Elsevier DOI 9605
BibRef

Acton, S.T.,
On Unsupervised Segmentation of Remotely-Sensed Imagery Using Nonlinear-Regression,
JRS(17), No. 7, May 10 1996, pp. 1407-1415. 9605
BibRef

Hofmann, T.[Thomas], and Buhmann, J.M.[Joachim M.],
Pairwise Data Clustering by Deterministic Annealing,
PAMI(19), No. 1, January 1997, pp. 1-14.
IEEE DOI 9702
BibRef
And: Correction: PAMI(19), No. 2, February 1997, pp. 192-192.
IEEE DOI A means to partition a data set and find hidden structure.
See also Unsupervised Texture Segmentation in a Deterministic Annealing Framework. BibRef

Bigün, J.,
Unsupervised Feature Reduction in Image Segmentation by Local Transforms,
PRL(14), 1993, pp. 573-583. BibRef 9300
Earlier:
Unsupervised feature reduction in image segmentation by local Karhunen-Loeve transform,
ICPR92(II:79-83).
IEEE DOI 9208
BibRef

Soh, L.K., Tsatsoulis, C.,
Unsupervised segmentation of ERS and Radarsat sea ice images using multiresolution peak detection and aggregated population equalization,
JRS(20), No. 15/16, October 1999, pp. 3087. BibRef 9910

Tang, M.[Ming], Ma, S.D.[Song-De],
General Scheme of Region Competition Based on Scale Space,
PAMI(23), No. 12, December 2001, pp. 1366-1378.
IEEE DOI 0112
BibRef
And: Corrections: PAMI(24), No. 7, July 2002, pp. 1007.
IEEE DOI 0207
BibRef
Earlier:
A Fast Algorithm of Multiresolution Elastic Matching,
SCIA97(xx-yy)
HTML Version. 9705
Region segmentation. Cluster then group clusters.
See also Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation. BibRef

Tang, M.[Ming], Xiao, J.[Jing], Ma, S.D.[Song-De],
Semantically Homogeneous Segmentation with Nonparametric Region Competition,
ICPR00(Vol I: 648-651).
IEEE DOI 0009
BibRef

Heiler, M.[Matthias], Schnörr, C.[Christoph],
Natural Image Statistics for Natural Image Segmentation,
IJCV(63), No. 1, June 2005, pp. 5-19.
Springer DOI 0501
BibRef
Earlier: ICCV03(1259-1266).
IEEE DOI 0311
Award, Marr Prize. BibRef

Keuchel, J.[Jens], Schnorr, C.[Chrostoph], Schellewald, C.[Christian], Cremers, D.[Daniel],
Binary partitioning, perceptual grouping, and restoration with semidefinite programming,
PAMI(25), No. 11, November 2003, pp. 1364-1379.
IEEE Abstract. 0311
BibRef
Earlier:
Unsupervised Image Partitioning with Semidefinite Programming,
DAGM02(141 ff.).
Springer DOI 0303
Figure-ground. Segment into coherent parts. BibRef

Heiler, M.[Matthias], Keuchel, J.[Jens], Schnörr, C.[Christoph],
Semidefinite Clustering for Image Segmentation with A-priori Knowledge,
DAGM05(309).
Springer DOI 0509
Award, GCPR, HM. BibRef
Earlier: A2, A1, A3:
Hierarchical Image Segmentation Based on Semidefinite Programming,
DAGM04(120-128).
Springer DOI 0505
BibRef

Keuchel, J.[Jens], Küttel, D.[Daniel],
Efficient Combination of Probabilistic Sampling Approximations for Robust Image Segmentation,
DAGM06(41-50).
Springer DOI 0610
BibRef

Keuchel, J.[Jens],
Multiclass Image Labeling with Semidefinite Programming,
ECCV06(II: 454-467).
Springer DOI 0608
BibRef

Tortorella, F.[Francesco],
A ROC-based reject rule for dichotomizers,
PRL(26), No. 2, 15 January 2005, pp. 167-180.
Elsevier DOI 0501
Two classes. BibRef

Jeon, B.K.[Byoung-Ki], Jung, Y.B.[Yun-Beom], Hong, K.S.[Ki-Sang],
Image Segmentation by Unsupervised Sparse Clustering,
PRL(27), No. 14, 15 October 2006, pp. 1650-1664.
Elsevier DOI 0609
BibRef
Earlier: WACV05(I: 2-7).
IEEE DOI 0502
Positiveness; Sparse clustering; Binary tree; Model selection; Intra- and inter-cluster measures Cluster into regions based on positiveness. BibRef

Cariou, C.[Claude], Chehdi, K.[Kacem],
Unsupervised texture segmentation/classification using 2-D autoregressive modeling and the stochastic expectation-maximization algorithm,
PRL(29), No. 7, 1 May 2008, pp. 905-917.
Elsevier DOI 0804
Image segmentation; Classification; Texture; Stochastic modeling; Parameter estimation; Remote sensing BibRef

Rosenberger, C., Chehdi, K.,
Unsupervised Clustering Method with Optimal Estimation of the Number of Clusters: Application to Image Segmentation,
ICPR00(Vol I: 656-659).
IEEE DOI 0009
BibRef

Choy, S.K., Tang, M.L., Tong, C.S.,
Image Segmentation Using Fuzzy Region Competition and Spatial/Frequency Information,
IP(20), No. 6, June 2011, pp. 1473-1484.
IEEE DOI 1106
BibRef

Elhamifar, E.[Ehsan], Vidal, R.[Rene],
Sparse Subspace Clustering: Algorithm, Theory, and Applications,
PAMI(35), No. 11, 2013, pp. 2765-2781.
IEEE DOI 1309
BibRef
Earlier:
Robust classification using structured sparse representation,
CVPR11(1873-1879).
IEEE DOI 1106
BibRef
Earlier:
Sparse subspace clustering,
CVPR09(2790-2797).
IEEE DOI 0906
Clustering algorithms. BibRef

Elhamifar, E.[Ehsan], Sapiro, G.[Guillermo], Sastry, S.S.,
Dissimilarity-Based Sparse Subset Selection,
PAMI(38), No. 11, November 2016, pp. 2182-2197.
IEEE DOI 1610
Approximation algorithms BibRef

Elhamifar, E.[Ehsan], Sapiro, G.[Guillermo], Vidal, R.[Rene],
See all by looking at a few: Sparse modeling for finding representative objects,
CVPR12(1600-1607).
IEEE DOI 1208
BibRef

Liang, X.P.[Xian-Peng], Huang, D.S.[De-Shuang],
Image segmentation fusion using weakly supervised trace-norm multi-task learning method,
IET-IPR(12), No. 7, July 2018, pp. 1079-1085.
DOI Link 1806
BibRef

Xiong, D.[Dehui], He, C.[Chu], Liu, X.L.[Xin-Long], Liao, M.S.[Ming-Sheng],
An End-To-End Bayesian Segmentation Network Based on a Generative Adversarial Network for Remote Sensing Images,
RS(12), No. 2, 2020, pp. xx-yy.
DOI Link 2001
BibRef

He, C.[Chu], Li, S.L.[Sheng-Lin], Xiong, D.[Dehui], Fang, P.Z.[Pei-Zhang], Liao, M.S.[Ming-Sheng],
Remote Sensing Image Semantic Segmentation Based on Edge Information Guidance,
RS(12), No. 9, 2020, pp. xx-yy.
DOI Link 2005
BibRef

Zhang, X.Q.[Xiao-Qian], Luo, C.[Chao], Wang, X.[Xiao], Li, J.H.[Jing-Hao], Zhao, S.[Shuai], Jiang, D.[Daojian],
Learnable Tensor Graph Fusion Framework for Natural Image Segmentation,
MultMed(26), 2024, pp. 7160-7173.
IEEE DOI 2405
Image segmentation, Tensors, Watersheds, Feature extraction, Transforms, Correlation, Image color analysis, Image segmentation, spectral clustering BibRef

Wang, B.[Bo], Tu, Z.W.[Zhuo-Wen],
Affinity learning via self-diffusion for image segmentation and clustering,
CVPR12(2312-2319).
IEEE DOI 1208
BibRef

Bertelli, L.[Luca], Yu, T.L.[Tian-Li], Vu, D.[Diem], Gokturk, B.[Burak],
Kernelized structural SVM learning for supervised object segmentation,
CVPR11(2153-2160).
IEEE DOI 1106
BibRef

Franek, L.[Lucas], Jiang, X.Y.[Xiao-Yi],
Evolutionary Weighted Mean Based Framework for Generalized Median Computation with Application to Strings,
SSSPR12(70-78).
Springer DOI 1211
BibRef

Franek, L.[Lucas], Jiang, X.Y.[Xiao-Yi],
Alternating Scheme for Supervised Parameter Learning with Application to Image Segmentation,
CAIP11(I: 118-125).
Springer DOI 1109
BibRef
Earlier:
Adaptive Parameter Selection for Image Segmentation Based on Similarity Estimation of Multiple Segmenters,
ACCV10(II: 697-708).
Springer DOI 1011
BibRef

Franek, L.[Lucas], Abdala, D.D.[Daniel Duarte], Vega-Pons, S.[Sandro], Jiang, X.Y.[Xiao-Yi],
Image Segmentation Fusion Using General Ensemble Clustering Methods,
ACCV10(IV: 373-384).
Springer DOI 1011
BibRef

Xu, K.[Kai], Wu, F.F.[Fang-Fang], Qin, K.[Kun],
An image segmentation method based on Type-2 fuzzy Gaussian Mixture Models,
IASP10(363-366).
IEEE DOI 1004
BibRef

Xiao, Z.H.[Zhi-Heng], Shi, J.[Jun], Chang, Q.[Qian],
Image Segmentation with Simplified PCNN,
CISP09(1-4).
IEEE DOI 0910
BibRef

Franti, P., Virmajoki, O., Kaukoranta, T.,
Branch-and-bound technique for solving optimal clustering,
ICPR02(II: 232-235).
IEEE DOI 0211
BibRef

Baggenstoss, P.M.,
The chain-rule processor: Optimal Classification Through Signal Processing,
ICPR02(I: 230-234).
IEEE DOI 0211
BibRef

Baggenstoss, P.M., Niemann, H.,
A Theoretically Optimal Probabilistic Classifier Using Class-specific Features,
ICPR00(Vol II: 763-768).
IEEE DOI 0009
BibRef

Comaniciu, D.[Dorin], Ramesh, V.[Visvanathan], del Bue, A.[Alessio],
Multivariate Saddle Point Detection for Statistical Clustering,
ECCV02(III: 561 ff.).
Springer DOI 0205
BibRef

Comaniciu, D.,
Image segmentation using clustering with saddle point detection,
ICIP02(III: 297-300).
IEEE DOI 0210
BibRef

Fontaine, M.[Michael], Macaire, L.[Ludovic], Postaire, J.G.[Jack-Gerard],
Unsupervised Segmentation Based on Connectivity Analysis,
ICPR00(Vol I: 660-663).
IEEE DOI 0009
BibRef

Kam, A.H., Fitzgerald, W.J.,
A General Method for Unsupervised Segmentation of Images Using a Multiscale Approach,
ECCV00(II: 69-84).
Springer DOI 0003
BibRef
Earlier:
Unsupervised multiscale image segmentation,
CIAP99(316-321).
IEEE DOI 9909
BibRef

Guo, G.D.[Guo-Dong], Yu, S.[Shan], Ma, S.D.[Song-De],
Unsupervised Segmentation Based on Multi-Resolution Analysis, Robust Statistics and Majority Game Theory,
ICPR98(Vol I: 799-801).
IEEE DOI 9808
BibRef

Iivarinen, J., Rauhamaa, J., Visa, A.,
Unsupervised Segmentation of Surface Defects,
ICPR96(IV: 356-360).
IEEE DOI 9608
(Helsinki Univ. of Technology., SF) BibRef

Kumar, V., Manolakos, E.[Elias],
Unsupervised Model-Based Object Recognition by Parameter Estimation of Hierarchical Mixtures,
ICIP96(III: 967-970).
IEEE DOI BibRef 9600

Rouquet, C.[Catherine], Bonton, P.[Pierre],
Region-based segmentation of textured images,
CIAP95(11-16).
Springer DOI 9509
BibRef

Derras, M., Debain, C., Berducat, M., Bonton, P., Gallice, J.,
Unsupervised Regions Segmentation: Real Time Control of an Upkeep Machine of Natural Spaces,
ECCV94(B:207-212).
Springer DOI BibRef 9400

Horita, Y., Murai, T., Miyahara, M.,
Region segmentation using K-mean clustering and genetic algorithms,
ICIP94(III: 1016-1020).
IEEE DOI 9411
BibRef

Chapter on 2-D Region Segmentation Techniques, Snakes, Active Contours continues in
Neural Networks for Segmentation .