Keith Price Bibliography Clustering for Region Segmentation

8.3.4 Clustering for Region Segmentation

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

Haralick, R.M., and Kelly, G.,
Pattern Recognition with Measurement Space and Spatial Clustering for Multiple Images,
PIEEE(57), No. 4, April 1969, pp. 654-665. BibRef 6904

Eklundh, J.O., Yamamoto, H., and Rosenfeld, A.,
A Relaxation Method for Multispectral Pixel Classification,
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.,
Classification of Multispectral Images using Associative Nets,
ICPR86(1240-1243). BibRef 8600

Eklundh, J.O.,
A Structured Approach to Segmentation of Aerial Photographs,
SPIE(397), Applications of Digital Image Processing, April 1983, pp. 21-27. BibRef 8304

Bentley, J.L.,
A Parallel Algorithm For Constructing Minimum Spanning Trees,
Algorithms(1), 1980, pp. 51-59. Minimal Spanning Tree. BibRef 8000

Fukada, Y.,
Spatial Clustering Procedures for Region Analysis,
PR(12), No. 6, 1980, pp. 395-403.
WWW Version. BibRef 8000

Velasco, F.R.D.,
A Method for the Analysis of Gaussian-Like Clusters,
PR(12), No. 6, 1980, pp. 381-393.
WWW Version. BibRef 8000

Sarabi, A., and Aggarwal, J.K.,
Segmentation of Chromatic Images,
PR(13), No. 6, 1981, pp. 417-427.
WWW Version. BibRef 8100

Swain, P.H.[Philip H.], Vardeman, S.B.[Stephen B.], Tilton, J.C.[James C.],
Contextual Classification of Multipsectral Image Data,
PR(13), No. 6, 1981, pp. 429-441.
WWW Version. BibRef 8100

Dondes, P.A., and Rosenfeld, A.,
Pixel Classification Based on Gray Level and Local 'Busyness',
PAMI(4), No. 1, January 1982, pp. 79-84. BibRef 8201

Dunn, S., Janos, L., Rosenfeld, A.,
Bimean Clustering,
PRL(1), 1983, pp. 169-173. BibRef 8300

Blanz, W.E., Reinhardt, E.R.,
Image Segmentation by Pixel Classification,
PR(13), No. 4, 1981, pp. 293-298.
WWW Version. BibRef 8100

Sclove, S.L.,
Application of the Conditional Population-Mixture Model to Image Segmentation,
PAMI(5), No. 4, July 1983, pp. 428-433. BibRef 8307
And: Reply to Comments: PAMI(6), No. 5, September 1984, pp. 657-658. BibRef

Titterington, D.M.,
Comments on 'Application of the Conditional Population-Mixture Model to Image Segmentation',
PAMI(6), No. 5, September 1984, pp. 656-657. BibRef 8409

Huntsberger, T.L., Jacobs, C.L., Cannon, R.L.,
Iterative Fuzzy Image Segmentation,
PR(18), No. 2, 1985, pp. 131-138.
WWW Version. BibRef 8500

Markham, K.C.,
Some Segmentation Processes for Application with a Spoke Filter,
PRL(5), 1987, pp. 329-335. BibRef 8700

Amadasun, M., King, R.A.,
Low-Level Segmentation of Multispectral Images via Agglomerative Clustering of Uniform Neighbourhoods,
PR(21), No. 3, 1988, pp. 261-268.
WWW Version. 0309
BibRef

Geong, D.S., and Lapsa, P.M.,
Unified Approach for Early-Phase Image Understanding Using a General Decision Criterion,
PAMI(11), No. 4, April 1989, pp. 357-371.
IEEE Abstract. IEEE Top Reference.
WWW Version. BibRef 8904

Zhang, J., and Modestino, J.W.,
A Model-Fitting Approach to Cluster Validation with Application to Stochastic Model-Based Image Segmentation,
PAMI(12), No. 10, October 1990, pp. 1009-1017.
IEEE Abstract. IEEE Top Reference.
WWW Version. BibRef 9010

Langan, D.A., Modestino, J.W., Zhang, J.,
Cluster Validation for Unsupervised Stochastic Model-Based Image Segmentation,
IP(7), No. 2, February 1998, pp. 180-195.
IEEE DOI Link 9802
BibRef
Earlier: ICIP94(II: 197-201).
IEEE DOI Link 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],
Robust Clustering with Applications in Computer Vision,
PAMI(13), No. 8, August 1991, pp. 791-802.
IEEE Abstract. IEEE Top Reference.
WWW Version. Robust Technique. BibRef 9108

Jolion, J.M.[Jean-Michel], Meer, P.[Peter], and Rosenfeld, A.[Azriel],
Generalized Minimum Volume Ellipsoid Clustering with Applications in Computer Vision,
Robust90(339-351). BibRef 9000

Bataouche, S.[Samira], and Jolion, J.M.[Jean-Michel],
A Hierarchical and Robust Process for Information Retrieval,
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,
CompAgri(9), 1993, pp. 53-70. Generate classes from one image and use for the rest of sequnce. BibRef 9300

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 Version. 9109
BibRef

Uchiyama, T., and Arbib, M.A.,
Color Image Segmentation Using Competitive Learning,
PAMI(16), No. 12, December 1994, pp. 1197-1206.
IEEE Abstract. IEEE Top Reference.
WWW Version. BibRef 9412
Earlier:
Object Extraction System from a Color Image,
IAS93(xx-yy). Generate clusters in color space. BibRef

Schroeter, P., Bigün, J.,
Hierarchical Image Segmentation by Multidimensional Clustering and Orientation-Adaptive Boundary Refinement,
PR(28), No. 5, May 1995, pp. 695-709.
WWW Version. Problems due to different size regions. BibRef 9505

Pappas, T.N., and Jayant, N.S.,
An Adaptive Clustering Algorithm for Image Segmentation,
ICCV88(310-315).
IEEE Abstract. IEEE Top Reference. BibRef 8800

Krishnapuram, R., and Freg, C.P.,
Fuzzy Algorithms to Find Linear and Planar Clusters and Their Application,
CVPR91(426-431).
IEEE Abstract. IEEE Top Reference. BibRef 9100

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 Version. 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 Version. BibRef 9409

Zheng, Y.J.,
Feature-Extraction and Image Segmentation Using Self-Organizing Networks,
MVA(8), No. 5, 1995, pp. 262-274.
HTML Version. BibRef 9500

Simpson, J.J., Keller, R.H.,
An Improved Fuzzy-Logic Segmentation of Sea-Ice, Clouds, and Ocean in Remotely-Sensed Arctic Imagery,
RSE(54), No. 3, December 1995, pp. 290-312. BibRef 9512

Olariu, S., Rao, N.S.V.,
Simple Algorithms for Some Classification Problems,
PRL(17), No. 2, February 8 1996, pp. 163-167. BibRef 9602

Rao, N.S.V., Oblow, E.M., Glover, C.W.,
Learning Separations By Boolean Combinations Of Half-Spaces,
PAMI(16), No. 7, July 1994, pp. 765-768.
IEEE Abstract. IEEE Top Reference.
WWW Version. BibRef 9407
Earlier: ICPR92(II:603-606).
IEEE DOI Link 9208
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 Link 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 Version. 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 Link 0803
BibRef

Pieczynski, W.[Wojciech],
Statistical Image Segmentation,
MGV(1), No. 1-2, 1992, pp. 261-268. BibRef 9200

Pieczynski, W.[Wojciech],
Hidden Evidential Markov Trees and Image Segmentation,
ICIP99(I:338-342).
IEEE Abstract. IEEE Top Reference. BibRef 9900

Salzenstein, F., Collet, C., Petremand, M.,
Champs de Markov Flous pour Imagerie Multispectrale-Fuzzy Markov Random Fields for Multispectral Images,
Traitement du Signal(21), No. 1, 2004, pp. 37-55. BibRef 0400

Derrode, S., Mercier, G., Pieczynski, W.,
Unsupervised multicomponent image segmentation combining a vectorial HMC Model and ICA,
ICIP03(II: 407-410).
IEEE Abstract. IEEE Top Reference. 0312
BibRef

Kudo, M., Yanagi, S., Shimbo, M.,
Construction of Class Regions by a Randomized Algorithm: A Randomized Subclass Method,
PR(29), No. 4, April 1996, pp. 581-588.
WWW Version. BibRef 9604

Jain, A.K.[Anil K.], and Flynn, P.J.[Patrick J.],
Image Segmentation Using Clustering,
AIU96(65-83). BibRef 9600

Tanaka, E.,
A Metric between Unrooted and Unordered Trees and Its Bottom-Up Computing Method,
PAMI(16), No. 12, December 1994, pp. 1233-1238.
IEEE Abstract. IEEE Top Reference.
WWW Version. 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
BibRef

Abrantes, A.J., Marques, J.S.,
Class of Constrained Clustering Algorithms for Object Boundary Extraction,
IP(5), No. 11, November 1996, pp. 1507-1521.
IEEE DOI Link 9611
BibRef

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 Abstract. IEEE Top Reference.
WWW Version. 9705
Segmentation using clustering of subregions. BibRef

Ceballos, J.C., Bottino, M.J.,
The Discrimination of Scenes by Principal Components-Analysis of Multispectral Imagery,
JRS(18), No. 11, July 20 1997, pp. 2437-2449. 9708
BibRef

Gupta, L., Sortrakul, T.,
A Gaussian-Mixture-Based Image Segmentation Algorithm,
PR(31), No. 3, March 1998, pp. 315-325.
WWW Version. 9802
BibRef

Kubota, T., Huntsberger, T.,
Adaptive Pattern-Recognition System for Scene Segmentation,
OptEng(37), No. 3, March 1998, pp. 829-835. 9804
BibRef

Kartikeyan, B., Sarkar, A., Majumder, K.L.,
A Segmentation Approach to Classification of Remote Sensing Imagery,
JRS(19), No. 9, June 1998, pp. 1695-1709. 9807
BibRef

Bianchi, N., Bottoni, P., Mussio, P., Spinu, C., Garbay, C.,
Situated Image Understanding in a Multiagent Framework,
PRAI(12), No. 5, August 1998, pp. 595-624. 9809
BibRef

Bianchi, N., Bottoni, P., Spinu, C., Garbay, C., Mussio, P.,
A Dynamical Organisation for Situated Image Interpretation,
ICPR96(I: 228-232).
IEEE DOI Link 9608
(Univ. degli Studi di Roma, I) BibRef

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 Version. 9808
BibRef
Earlier:
Segmentation of 2D and 3D Images Through a Hierarchical Clustering Based on Region Modelling,
ICIP97(III: 50-53).
IEEE DOI Link 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 Link 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.
IEEE Top Reference. BibRef 9603

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,
MVA(11), No. 6, 2000, pp. 39-60.
HTML Version. 0005
BibRef
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).
WWW Version. 9909
BibRef
Earlier:
Correction of Systematic Errors in Automatically Produced Boundaries from Low Contrast Ventriculograms,
ICPR96(IV: 361-365).
IEEE DOI Link 9608
(Univ. of Washington, USA) BibRef

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
BibRef

Rhouma, M.B.H.[Mohamed Ben Hadj], Frigui, H.[Hichem],
Self-Organization of Pulse-Coupled Oscillators with Application to Clustering,
PAMI(23), No. 2, February 2001, pp. 180-195.
IEEE Abstract. IEEE Top Reference.
WWW Version. 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.
HTML Version. 0105
BibRef

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
BibRef
And: Correction: GeoRS(40), No. 1, January 2002, pp. 229-229.
IEEE Top Reference. 0203
BibRef
Earlier:
Multiscale Texture Segmentation Using Hybrid Contextual Labeling Tree,
ICIP00(Vol III: 576-579).
IEEE Abstract. IEEE Top Reference. 0008
BibRef

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
BibRef
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 Link 9810
BibRef

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
BibRef

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.
WWW Version. 0411
BibRef

Martínez, A.M.[Aleix M.], Mittrapiyanuruk, P.[Pradit], Kak, A.C.[Avinash C.],
On combining graph-partitioning with non-parametric clustering for image segmentation,
CVIU(95), No. 1, July 2004, pp. 72-85.
WWW Version. 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 Link 0512
BibRef
Earlier: ICPR04(II: 106-109).
IEEE DOI Link 0409
BibRef

Farmer, M.E.[Michael E.],
Application of the wrapper framework for image object detection,
ICPR08(1-4).
IEEE DOI Link 0812
BibRef

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.
WWW Version. 0611
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 Link 0701
BibRef
Earlier:
Efficiently Segmenting Images with Dominant Sets,
ICIAR04(I: 17-24).
WWW Version. 0409
BibRef
Earlier:
Dominant sets and hierarchical clustering,
ICCV03(362-369).
IEEE DOI Link 0311
BibRef
And:
A new graph-theoretic approach to clustering and segmentation,
CVPR03(I: 145-152).
IEEE Abstract. IEEE Top Reference. 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 Link 0908
BibRef

Sperotto, A.[Anna], Pelillo, M.[Marcello],
Szemerédi's Regularity Lemma and Its Applications to Pairwise Clustering and Segmentation,
EMMCVPR07(13-27).
Springer DOI Link 0708
BibRef

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 Link 0706
BibRef
Earlier:
Shape constrained image segmentation by parametric distributional clustering,
CVPR04(I: 386-393).
IEEE Abstract. IEEE Top Reference. 0408
BibRef

Hermes, L.[Lothar], Zöller, T.[Thomas], Buhmann, J.M.[Joachim M.],
Parametric Distributional Clustering for Image Segmentation,
ECCV02(III: 577 ff.).
HTML Version. 0205
BibRef

Hermes, L., Buhmann, J.M.,
Contextual Classification by Entropy-Based Polygonization,
CVPR01(II:442-447).
IEEE Abstract. IEEE Top Reference. 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.
WWW Version. 0710
BibRef
Robust Path-Based Spectral Clustering with Application to Image Segmentation,
ICCV05(I: 278-285).
IEEE DOI Link 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 Link 0810
BibRef

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 Link
WWW Version. 0905
BibRef
Earlier: A1, A3, A2, A4:
Improved Adaptive Spatial Information Clustering for Image Segmentation,
ISVC08(I: 308-317).
Springer DOI Link 0812
Spatial clustering; Image segmentation; Information-theoretic approach BibRef

Ganz, M.[Melanie], Loog, M.[Marco], Brandt, S.[Sami], Nielsen, M.[Mads],
Dense iterative contextual pixel classification using Kriging,
MMBIA09(87-93).
IEEE DOI Link 0906
Segmentation using context. BibRef

Alaoui, M.T.[Mohammed Talibi], Sbihi, A.[Abderrahmane],
A New Clustering Algorithm for Color Image Segmentation,
IbPRIA09(217-224).
Springer DOI Link 0906
BibRef

Chen, Y.W.[Yen-Wei], Han, X.H.[Xian-Hua],
Supervised Local Subspace Learning for Region Segmentation and Categorization in High-Resolution Satellite Images,
CCIW09(226-233).
Springer DOI Link 0903
BibRef

Shah, H.[Hina], Mitra, S.K.[Suman K.], Banerjee, A.[Asim],
Information Slicing: An Application to Object Classification in Satellite Images,
ICCVGIP08(458-465).
IEEE DOI Link 0812
BibRef