14.2.6 Iterative, Hierarchical Clustering Techniques

Chapter Contents (Back)
Hierarchical Classification. Iterative Classification. Clustering, Iterative. Clustering, Hierarchical. 9905

Zobrist, A.L.[Albert L.],
The organization of extracted features for pattern recognition,
PR(3), No. 1, April 1971, pp. 23-30.
WWW Link. 0309
A two layered pattern recognition program. The first layer scans an input for features and produces a coded representation. The second layer looks for combinations of code which signify relations between features in the input. BibRef

Haralick, R.M.[Robert M.], Dinstein, I.,
An Iterative Clustering Procedure,
), SMC(1), No. 3, July, 1971, pp. 275-289. BibRef 7107

Biehl, L.L., and Silva, L.F.,
A Multilevel Multispectral Data Set Analysis in the Visible and Infrared Wavelength Regions,
PIEEE(63), No. 1, 1975, pp. 164-175. BibRef 7500

Lukasová, A.[Alena],
Hierarchical agglomerative clustering procedure,
PR(11), No. 5-6, 1979, pp. 365-381.
WWW Link. 0309
BibRef

Smith, S.P.[Stephen P.], Dubes, R.C.[Richard C.],
Stability of a hierarchical clustering,
PR(12), No. 3, 1980, pp. 177-187.
WWW Link. 0309
BibRef

Ozawa, K.[Kazumasa],
Classic: A hierarchical clustering algorithm based on asymmetric similarities,
PR(16), No. 2, 1983, pp. 201-211.
WWW Link. 0309
BibRef

Ozawa, K.[Kazumasa],
A stratificational overlapping cluster scheme,
PR(18), No. 3-4, 1985, pp. 279-286.
WWW Link. 0309
BibRef

Perruchet, C.[Christophe],
Constrained agglomerative hierarchical classification,
PR(16), No. 2, 1983, pp. 213-217.
WWW Link. 0309
BibRef

Schuermann, J.[Juergen], Doster, W.[Wolfgang],
A decision theoretic approach to hierarchical classifier design,
PR(17), No. 3, 1984, pp. 359-369.
WWW Link. 0309
BibRef

López de Mŕntaras, R., Aguilar-Martín, J.,
Self-learning pattern classification using a sequential clustering technique,
PR(18), No. 3-4, 1985, pp. 271-277.
WWW Link. 0309
BibRef

Jain, N.C.[Naresh C.], Indrayan, A.[Abhaya], Goel, L.R.[Lajpat R.],
Monte Carlo comparison of six hierarchical clustering methods on random data,
PR(19), No. 1, 1986, pp. 95-99.
WWW Link. 0309
BibRef

Plastria, F.[Frank],
Two hierarchies associated with each clustering scheme,
PR(19), No. 2, 1986, pp. 193-196.
WWW Link. 0309
BibRef

Kurzynski, M.W.[Marek W.],
On the Identity of Optimal Strategies for Multistage Classifiers,
PRL(10), No. 1, 1989, pp. 39-46. See also optimal strategy of a tree classifier, The. See also On the multistage Bayes classifier. BibRef 8900

Kurzynski, M.W., Puchala, E., Blinowska, A.,
A branch-and-bound algorithm for optimization of multiperspective classifier,
ICPR94(B:235-239).
IEEE DOI 9410
BibRef

Kurzynski, M.W., Puchala, E.,
Algorithms of the multiperspective pattern recognition,
ICPR92(II:627-630).
IEEE DOI 9208
BibRef

Spivak, S.[Shalom],
A multisurface method for pattern classification,
PR(22), No. 5, 1989, pp. 587-591.
WWW Link. 0309
The desired classifier is built in two stages. In the first stage an initial classifier is built. In the second stage the classifier is modified to obtain the desired properties. Modification is performed by use of the gradient descent procedure. BibRef

Spivak, S.[Shalom],
Some properties of the multisurface method for pattern classification,
PR(24), No. 4, 1991, pp. 325-330.
WWW Link. 0401
BibRef

Li, X.,
Parallel algorithms for hierarchical clustering and cluster validity,
PAMI(12), No. 11, November 1990, pp. 1088-1092.
IEEE DOI 0401
BibRef

Murtagh, F.,
Comments on 'Parallel algorithms for hierarchical clustering and cluster validity',
PAMI(14), No. 10, October 1992, pp. 1056-1057.
IEEE DOI 0401
BibRef

Zhang, Q.W.[Qi-Wen], Boyle, R.D.[Roger D.],
A new clustering algorithm with multiple runs of iterative procedures,
PR(24), No. 9, 1991, pp. 835-848.
WWW Link. 0401
BibRef

Chan, K.P., Cheung, Y.S.,
Clustering of clusters,
PR(25), No. 2, February 1992, pp. 211-217.
WWW Link. 0401
Character recognition. BibRef

Liaw, J.N.[Jin-Nan], Kashyap, R.L.,
A new sequential classifier using information criterion window,
PR(27), No. 10, October 1994, pp. 1423-1438.
WWW Link. 0401
BibRef

Zavaljevski, A., Dhawan, A.P., Kelch, D.J., Riddell, J.,
Adaptive Multilevel Classification and Detection in Multispectral Images,
OptEng(35), No. 10, October 1996, pp. 2884-2893. 9611
BibRef

El-Sonbaty, Y.[Yasser], Ismail, M.A.,
On-line hierarchical clustering,
PRL(19), No. 14, December 1998, pp. 1285-1291. BibRef 9812

Fisher, D.,
Iterative Optimization and Simplification of Hierarchical Clusterings,
JAIR(4), 1996, pp. 147-178.
HTML Version. BibRef 9600

Labonté, G.,
On a Neural Network that Performs an Enhanced Nearest-Neighbour Matching,
PAA(3), No. 3 2000, pp. 267-278. 0010
BibRef

Leung, Y.[Yee], Zhang, J.S.[Jiang-She], Xu, Z.B.[Zong-Ben],
Clustering by Scale-Space Filtering,
PAMI(22), No. 12, December 2000, pp. 1396-1410.
IEEE DOI 0012
BibRef

Leung, Y.[Yee], Ma, J.H.[Jiang-Hong], Zhang, W.X.[Wen-Xiu],
A New Method for Mining Regression Classes in Large Data Sets,
PAMI(23), No. 1, January 2001, pp. 5-21.
IEEE DOI 0101
Genetic algorithm. Regression class: subset of data subject to regression model. BibRef

Talavera, L.[Luis], Béjar, J.[Javier],
Generality-Based Conceptual Clustering with Probabilistic Concepts,
PAMI(23), No. 2, February 2001, pp. 196-206.
IEEE DOI 0102
BibRef

Fieguth, P.W.[Paul W.],
Multiply-rooted multiscale models for large-scale estimation,
IP(10), No. 11, November 2001, pp. 1676-1686.
IEEE DOI 0201
BibRef
Earlier:
Foveated Multiscale Models for Large-Scale Estimation,
ICIP99(II:871-874).
IEEE DOI BibRef
Earlier:
Multipole-motivated reduced-state estimation,
ICIP98(I: 635-638).
IEEE DOI 9810
BibRef

Agnelli, D.[Davide], Bollini, A.[Alessandro], Lombardi, L.[Luca],
Image classification: an evolutionary approach,
PRL(23), No. 1-3, January 2002, pp. 303-309.
Elsevier DOI 0201
BibRef

Sun, Y.[Ying], Zhu, Q.M.[Qiu-Ming], Chen, Z.X.[Zheng-Xin],
An iterative initial-points refinement algorithm for categorical data clustering,
PRL(23), No. 7, May 2002, pp. 875-884.
Elsevier DOI 0203
BibRef

Lam, W.[Wai], Keung, C.K.[Chi-Kin], Liu, D.Y.[Dan-Yu],
Discovering Useful Concept Prototypes for Classification Based on Filtering and Abstraction,
PAMI(24), No. 8, August 2002, pp. 1075-1090.
IEEE Abstract. 0208
ICPL. Integrated Concept Prototype Learner. Integrate instance filtering with instace abstraction. BibRef

Fernández Prieto, D.,
An iterative approach to partially supervised classification problems,
JRS(23), No. 18, September 2002, pp. 3887-3892.
WWW Link. 0211
See also adaptive semiparametric and context-based approach to unsupervised change detection multitemporal remote-sensing images, An. BibRef

Rodríguez, C., Soraluze, I., Muguerza, J., Martín, J.I., Álvarez, G.,
Hierarchical classifiers based on neighbourhood criteria with adaptive computational cost,
PR(35), No. 12, December 2002, pp. 2761-2769.
WWW Link. 0209
BibRef

Vijaya, P.A., Murty, M.N.[M. Narasimha], Subramanian, D.K.,
Leaders-Subleaders: An efficient hierarchical clustering algorithm for large data sets,
PRL(25), No. 4, March 2004, pp. 505-513.
WWW Link. 0402
BibRef

Huber, R.[Reinhold], Ramoser, H.[Herbert], Mayer, K.[Konrad], Penz, H.[Harald], Rubik, M.[Michael],
Classification of coins using an eigenspace approach,
PRL(26), No. 1, 1 January 2005, pp. 61-75.
Elsevier DOI 0501
Multistage classifier for large classes of coins. BibRef

Hill, E.J.[E. June], Alder, M.D.[Michael D.], de Silva, C.J.S.[Christopher J.S.],
An improvement to the DR clustering algorithm,
PRL(26), No. 1, 1 January 2005, pp. 101-107.
WWW Link. 0501
Map data from a region to toroidal surface then run DR (Dog-Rabbit) clustering. BibRef

Lee, S., Crawford, M.M.,
Unsupervised Multistage Image Classification Using Hierarchical Clustering With a Bayesian Similarity Measure,
IP(14), No. 3, March 2005, pp. 312-320.
IEEE DOI 0501
BibRef

Milgram, J.[Jonathan], Sabourin, R.[Robert], Cheriet, M.[Mohamed],
Combining Model-based and Discriminative Approaches in a Modular Two-stage Classification System: Application to Isolated Handwritten Digit Recognition,
ELCVIA(5), No. 2, 2005, pp. 1-15.
WWW Link. 0505
BibRef
Earlier:
Two-stage classification system combining model-based and discriminative approaches,
ICPR04(I: 152-155).
IEEE DOI 0409
BibRef

Lee, J.W.T.[John W.T.], Yeung, D.S.[Daniel S.], Tsang, E.C.C.[Eric C.C.],
Hierarchical clustering based on ordinal consistency,
PR(38), No. 11, November 2005, pp. 1913-1925.
WWW Link. 0509
BibRef

Amador, J.J.[José J.],
Sequential clustering by statistical methodology,
PRL(26), No. 14, 15 October 2005, pp. 2152-2163.
WWW Link. 0510
BibRef

Dutta, M., Mahanta, A.K.[A. Kakoti], Pujari, A.K.[Arun K.],
QROCK: A quick version of the ROCK algorithm for clustering of categorical data,
PRL(26), No. 15, November 2005, pp. 2364-2373.
WWW Link. 0510
An agglomerative hierarchical clustering algorithm for clustering categorical data. BibRef

Zilong, G.[Guo], Sun'an, W.[Wang], Jian, Z.[Zhuang],
A novel immune evolutionary algorithm incorporating chaos optimization,
PRL(27), No. 1, 1 January 2006, pp. 2-8.
WWW Link. 0512
BibRef

Nock, R.[Richard], and Nielsen, F.[Frank],
On Weighting Clustering,
PAMI(28), No. 8, August 2006, pp. 1223-1235.
IEEE DOI 0606
BibRef
Earlier:
Improving clustering algorithms through constrained convex optimization,
ICPR04(IV: 557-560).
IEEE DOI 0409
Formalize unsupervised clustering ideas to take advantage of boosting ideas. BibRef

Nock, R.[Richard], Nielsen, F.[Frank],
Bregman Divergences and Surrogates for Learning,
PAMI(31), No. 11, November 2009, pp. 2048-2059.
IEEE DOI 0910
BibRef
Earlier: A2, A1:
Bregman sided and symmetrized centroids,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Nielsen, F.[Frank], Nock, R.[Richard],
Optimal Interval Clustering: Application to Bregman Clustering and Statistical Mixture Learning,
SPLetters(21), No. 10, October 2014, pp. 1289-1292.
IEEE DOI 1407
BibRef
Earlier:
Clustering Multivariate Normal Distributions,
ETVC08(164-174).
Springer DOI 0811
Dynamic programming BibRef

Nielsen, F.[Frank], Nock, R.[Richard],
Generalizing Skew Jensen Divergences and Bregman Divergences With Comparative Convexity,
SPLetters(24), No. 8, August 2017, pp. 1123-1127.
IEEE DOI 1708
matrix algebra, Bregman divergence, comparative convexity, generalizing skew Jensen divergence, monotone embeddings, ordinary convexity, quasiarithmetic means, Convex functions, Generators, Harmonic analysis, Indexes, Q measurement, Radio frequency, Taylor series, Bregman divergence (BD), conformal divergence, convexity, quasi-arithmetic weighted mean, regular mean, skew, Jensen, divergence, (JD) BibRef

Nock, R.[Richard], Nielsen, F.[Frank],
On the efficient minimization of convex surrogates in supervised learning,
ICPR08(1-4).
IEEE DOI 0812
BibRef
And:
Intrinsic Geometries in Learning,
ETVC08(175-215).
Springer DOI 0811
BibRef

Nielsen, F.[Frank], Nock, R.[Richard],
On the chi square and higher-order chi distances for approximating f-divergences,
SPLetters(21), No. 1, January 2014, pp. 10-13.
IEEE DOI 1402
Gaussian processes BibRef

Nock, R.[Richard], Vaillant, P.[Pascal], Henry, C.[Claudia], Nielsen, F.[Frank],
Soft memberships for spectral clustering, with application to permeable language distinction,
PR(42), No. 1, January 2009, pp. 43-53.
WWW Link. 0809
Spectral clustering; Soft membership; Stochastic processes; Text classification BibRef

Kushnir, D.[Dan], Galun, M.[Meirav], Brandt, A.[Achi],
Fast multiscale clustering and manifold identification,
PR(39), No. 10, October 2006, pp. 1876-1891.
WWW Link. 0606
Algebraic multigrid (AMG); Aggregation; Graph partitioning; Similarity-based clustering; Manifold; Data analysis; Astrophysical models BibRef

Kushnir, D.[Dan], Galun, M.[Meirav], Brandt, A.[Achi],
Efficient Multilevel Eigensolvers with Applications to Data Analysis Tasks,
PAMI(32), No. 8, August 2010, pp. 1377-1391.
IEEE DOI 1007
Solving systems of equations. Solve eigen value problems. BibRef

Martínez-Otzeta, J.M., Sierra, B., Lazkano, E., Astigarraga, A.,
Classifier hierarchy learning by means of genetic algorithms,
PRL(27), No. 16, December 2006, pp. 1998-2004.
WWW Link. 0611
Data mining; Classifier combination; Genetic algorithms BibRef

Martínez-Otzeta, J.M., Sierra, B., Lazkano, E., Jauregi, E., Yurramendi, Y.,
Analyzing Classifier Hierarchy Multiclassifier Learning,
CIARP08(775-782).
Springer DOI 0809
BibRef

Cao, F.[Frédéric], Delon, J.[Julie], Desolneux, A.[Agnčs], Musé, P.[Pablo], Sur, F.[Frédéric],
A Unified Framework for Detecting Groups and Application to Shape Recognition,
JMIV(27), No. 2, February 2007, pp. 91-119.
Springer DOI 0704
See also grouping principle and four applications, A. Evaluate validity of clusters, containment of clusters, merging of clusters. BibRef

Santos, J.M.[Jorge M.], de Sa, J.M.[Joaquim Marques], Alexandre, L.A.[Luis A.],
LEGClust: A Clustering Algorithm Based on Layered Entropic Subgraphs,
PAMI(30), No. 1, January 2008, pp. 62-75.
IEEE DOI 0711
Builds layers of subgraphs then applies clustering. BibRef

Goldberger, J.[Jacob], Tassa, T.[Tamir],
A hierarchical clustering algorithm based on the Hungarian method,
PRL(29), No. 11, 1 August 2008, pp. 1632-1638.
WWW Link. 0804
Grouping; Pairwise clustering; Hierarchical clustering; Graph algorithms BibRef

Dang, E.K.F.[Edward K. F.], Luk, R.W.P.[Robert W. P.], Lee, D.L.[Dik Lun], Ho, K.S.[Kei-Shiu], Chan, S.C.F.[Stephen C. F.],
Optimal Combination of Nested Clusters by a Greedy Approximation Algorithm,
PAMI(31), No. 11, November 2009, pp. 2083-2087.
IEEE DOI 0910
BibRef

Li, X.T.[Xu-Tao], Ye, Y.M.[Yun-Ming], Li, M.J.J.[Mark Jun-Jie], Ng, M.K.[Michael K.],
On cluster tree for nested and multi-density data clustering,
PR(43), No. 9, September 2010, pp. 3130-3143.
Elsevier DOI 1006
Hierarchical clustering; Multi-densities; Cluster tree; k-Means-type algorithm BibRef

Tang, X.Q.[Xu-Qing], Zhu, P.[Ping], Cheng, J.X.[Jia-Xing],
The structural clustering and analysis of metric based on granular space,
PR(43), No. 11, November 2010, pp. 3768-3786.
Elsevier DOI 1008
Granular computing; Granular space; Normalized metric space; Clustering structural analysis; Consistent cluster; Optimal cluster; Clustering fusion BibRef

Perina, A.[Alessandro], Cristani, M.[Marco], Castellani, U.[Umberto], Murino, V.[Vittorio], Jojic, N.[Nebojsa],
Free Energy Score Spaces: Using Generative Information in Discriminative Classifiers,
PAMI(34), No. 7, July 2012, pp. 1249-1262.
IEEE DOI 1205
Hybrid generative/discriminative paradigm, variational free energy, classification. Fixed dimension feature vector for each data sample of varying size. BibRef

Bicego, M., Perina, A., Murino, V., Martins, A.F.T., Aguiar, P.M.Q., Figueiredo, M.A.T.,
Combining free energy score spaces with information theoretic kernels: Application to scene classification,
ICIP10(2661-2664).
IEEE DOI 1009
Classifiers for structured objects. BibRef

Li, C.Z.[Chun-Zhong], Xu, Z.B.[Zong-Ben], Luo, T.[Tao],
A heuristic hierarchical clustering based on multiple similarity measurements,
PRL(34), No. 2, 15 January 2013, pp. 155-162.
Elsevier DOI 1212
Data mining; Agglomerative clustering; Heuristic; Blurring; Top-down; Structural nearest neighbor BibRef

Pérez-Suárez, A.[Airel], Martínez-Trinidad, J.F.[José Fco.], Carrasco-Ochoa, J.A.[Jesús A.], Medina-Pagola, J.E.[José E.],
An algorithm based on density and compactness for dynamic overlapping clustering,
PR(46), No. 11, November 2013, pp. 3040-3055.
Elsevier DOI 1306
Data mining; Clustering; Overlapping clustering algorithms; Dynamic clustering algorithms BibRef

Zhang, W.[Wei], Zhao, D.L.[De-Li], Wang, X.G.[Xiao-Gang],
Agglomerative clustering via maximum incremental path integral,
PR(46), No. 11, November 2013, pp. 3056-3065.
Elsevier DOI 1306
Agglomerative clustering; Path integral; Graph algorithms; Random walk BibRef

Kim, B.S.[Byung-Soo], Park, J.Y.[Jae Young], Gilbert, A.C.[Anna C.], Savarese, S.[Silvio],
Hierarchical classification of images by sparse approximation,
IVC(31), No. 12, 2013, pp. 982-991.
Elsevier DOI 1312
Sparse approximation BibRef

Kim, B.S.[Byung Soo], Park, J.Y.[Jae Young], Mohan, A.[Anush], Gilbert, A.C.[Anna C.], Savarese, S.[Silvio],
Hierarchical Classification of Images by Sparse Approximation 1,
BMVC11(xx-yy).
HTML Version. 1110
BibRef

Abin, A.A.[Ahmad Ali], Beigy, H.[Hamid],
Active selection of clustering constraints: a sequential approach,
PR(47), No. 3, 2014, pp. 1443-1458.
Elsevier DOI 1312
Active constraint selection BibRef

Abin, A.A.[Ahmad Ali], Beigy, H.[Hamid],
Active constrained fuzzy clustering: A multiple kernels learning approach,
PR(48), No. 3, 2015, pp. 953-967.
Elsevier DOI 1412
Constrained clustering BibRef

Abin, A.A.[Ahmad Ali],
Clustering with side information: Further efforts to improve efficiency,
PRL(84), No. 1, 2016, pp. 252-258.
Elsevier DOI 1612
Constrained clustering BibRef

Sharmila, T.S.[T. Sree], Ramar, K., Raja, T.S.R.[T. Sree Renga],
Impact of applying pre-processing techniques for improving classification accuracy,
SIViP(8), No. 1, January 2014, pp. 149-157.
WWW Link. 1402
BibRef

Zhu, S.[Shiai], Wei, X.Y.[Xiao-Yong], Ngo, C.W.[Chong-Wah],
Collaborative error reduction for hierarchical classification,
CVIU(124), No. 1, 2014, pp. 79-90.
Elsevier DOI 1406
Concept detection BibRef

Huang, X., Lu, Q., Zhang, L., Plaza, A.,
New Postprocessing Methods for Remote Sensing Image Classification: A Systematic Study,
GeoRS(52), No. 11, November 2014, pp. 7140-7159.
IEEE DOI 1407
Anisotropic magnetoresistance BibRef

Huang, S.J.[Sheng-Jun], Jin, R.[Rong], Zhou, Z.H.[Zhi-Hua],
Active Learning by Querying Informative and Representative Examples,
PAMI(36), No. 10, October 2014, pp. 1936-1949.
IEEE DOI 1410
learning (artificial intelligence) BibRef

Espinola, M., Piedra-Fernandez, J.A., Ayala, R., Iribarne, L., Wang, J.Z.,
Contextual and Hierarchical Classification of Satellite Images Based on Cellular Automata,
GeoRS(53), No. 2, February 2015, pp. 795-809.
IEEE DOI 1411
cellular automata BibRef

de Morsier, F.[Frank], Tuia, D.[Devis], Borgeaud, M.[Maurice], Gass, V.[Volker], Thiran, J.P.[Jean-Philippe],
Cluster validity measure and merging system for hierarchical clustering considering outliers,
PR(48), No. 4, 2015, pp. 1478-1489.
Elsevier DOI 1502
Clustering BibRef

de Morsier, F.[Frank], Borgeaud, M.[Maurice], Gass, V.[Volker], Thiran, J.P.[Jean-Philippe], Tuia, D.[Devis],
Kernel Low-Rank and Sparse Graph for Unsupervised and Semi-Supervised Classification of Hyperspectral Images,
GeoRS(54), No. 6, June 2016, pp. 3410-3420.
IEEE DOI 1606
hyperspectral imaging BibRef

Leski, J.M.[Jacek M.], Kotas, M.[Marian],
Hierarchical clustering with planar segments as prototypes,
PRL(54), No. 1, 2015, pp. 1-10.
Elsevier DOI 1502
Hierarchical clustering BibRef

Mall, R.[Raghvendra], Mehrkanoon, S.[Siamak], Suykens, J.A.K.[Johan A.K.],
Identifying intervals for hierarchical clustering using the Gershgorin circle theorem,
PRL(55), No. 1, 2015, pp. 1-7.
Elsevier DOI 1503
Gershgorin circle theorem BibRef

Lian, C.F.[Chun-Feng], Ruan, S.[Su], Denœux, T.[Thierry],
An evidential classifier based on feature selection and two-step classification strategy,
PR(48), No. 7, 2015, pp. 2318-2327.
Elsevier DOI 1504
Dempster-Shafer theory BibRef

Zhong, C.M.[Cai-Ming], Yue, X.D.[Xiao-Dong], Lei, J.S.[Jing-Sheng],
Visual hierarchical cluster structure: A refined co-association matrix based visual assessment of cluster tendency,
PRL(59), No. 1, 2015, pp. 48-55.
Elsevier DOI 1505
Hierarchical clustering BibRef

Najjar, A.[Alameen], Ogawa, T.[Takahiro], Haseyama, M.[Miki],
Bregman pooling: feature-space local pooling for image classification,
MultInfoRetr(4), No. 4, December 2015, pp. 247-259.
Springer DOI 1511
BibRef

Bakoben, M.[Maha], Bellotti, A.[Anthony], Adams, N.M.[Niall M.],
Improving clustering performance by incorporating uncertainty,
PRL(77), No. 1, 2016, pp. 28-34.
Elsevier DOI 1606
Clustering with uncertainty BibRef

Wu, H.[Hang], Liu, B.[Baozhen], Su, W.H.[Wei-Hua], Zhang, W.C.[Wen-Chang], Sun, J.G.[Jing-Gong],
Hierarchical Coding Vectors for Scene Level Land-Use Classification,
RS(8), No. 5, 2016, pp. 436.
DOI Link 1606
BibRef

Alsahwa, B.[Bassem], Solaiman, B.[Basel], Almouahed, S.[Shaban], Bossé, É.[Éloi], Guériot, D.[Didier],
Iterative Refinement of Possibility Distributions by Learning for Pixel-Based Classification,
IP(25), No. 8, August 2016, pp. 3533-3545.
IEEE DOI 1608
Markov processes BibRef

Zhuo, Z.L.[Zhong-Liu], Zhang, X.S.[Xiao-Song], Niu, W.[Weina], Yang, G.[Guowu], Zhang, J.Z.[Jing-Zhong],
Improving data field hierarchical clustering using Barnes-Hut algorithm,
PRL(80), No. 1, 2016, pp. 113-120.
Elsevier DOI 1609
Barnes-Hut algorithm BibRef

Hoyoux, T.[Thomas], Rodríguez-Sánchez, A.J.[Antonio J.], Piater, J.H.[Justus H.],
Can Computer Vision Problems Benefit from Structured Hierarchical Classification?,
MVA(27), No. 8, November 2016, pp. 1299-1312.
Springer DOI 1612
BibRef
Earlier: Add A4: Szedmak, S.[Sandor], CAIP15(II:403-414).
Springer DOI 1511
BibRef

Li, S.N.[Shao-Ning], Li, W.J.[Wen-Jing], Qiu, J.[Jia],
A Novel Divisive Hierarchical Clustering Algorithm for Geospatial Analysis,
IJGI(6), No. 1, 2017, pp. xx-yy.
DOI Link 1702
BibRef

Yang, C.X.[Chen-Xue], Ye, M.[Mao], Tang, S.[Song], Xiang, T.[Tao], Liu, Z.J.[Zi-Jian],
Semi-supervised low-rank representation for image classification,
SIViP(11), No. 1, January 2017, pp. 73-80.
Springer DOI 1702
BibRef

Cheng, F.Y.[Fei-Yang], He, X.M.[Xu-Ming], Zhang, H.[Hong],
Stacked Learning to Search for Scene Labeling,
IP(26), No. 4, April 2017, pp. 1887-1898.
IEEE DOI 1704
Cost function BibRef

Bertini Junior, J.R.[Joăo Roberto], do Carmo Nicoletti, M.[Maria],
Enhancing classification performance using attribute-oriented functionally expanded data,
PRL(89), No. 1, 2017, pp. 39-45.
Elsevier DOI 1704
Improving classification performance BibRef

Garcia-Piquer, A.[Alvaro], Bacardit, J.[Jaume], Fornells, A.[Albert], Golobardes, E.[Elisabet],
Scaling-up multiobjective evolutionary clustering algorithms using stratification,
PRL(93), No. 1, 2017, pp. 69-77.
Elsevier DOI 1706
Multiobjective evolutionary algorithms BibRef

Wang, L.G.[Lei-Guang], Huang, X.[Xin], Zheng, C.[Chen], Zhang, Y.[Yun],
A Markov random field integrating spectral dissimilarity and class co-occurrence dependency for remote sensing image classification optimization,
PandRS(128), No. 1, 2017, pp. 223-239.
Elsevier DOI 1706
Remote sensing image classification. MRF model for edge-preserving spatial regularization of classification maps. BibRef


José-García, A.[Adán], Gómez-Flores, W.[Wilfrido],
Evolutionary Clustering Using Multi-prototype Representation and Connectivity Criterion,
MCPR17(63-73).
Springer DOI 1706
BibRef

Hu, J.G.[Jia-Gao], Sun, Z.X.[Zheng-Xing], Li, B.[Bo], Wang, S.[Shuang],
PicMarker: Data-Driven Image Categorization Based on Iterative Clustering,
ACCV16(IV: 172-187).
Springer DOI 1704
BibRef

Karray, E., Loghmari, M.A., Naceur, M.S.,
High and low-level hierarchical classification as an efficient analysis of remotely sensed hyperpectral data,
ISIVC16(236-241)
IEEE DOI 1704
Hyperspectral imaging BibRef

Klein, D.A.[Dominik Alexander], Schulz, D.[Dirk], Cremers, A.B.[Armin Bernd],
Realtime Hierarchical Clustering Based on Boundary and Surface Statistics,
ACCV16(I: 3-19).
Springer DOI 1704
BibRef

Qian, B.[Bin], Shen, X.B.[Xiao-Bo], Gu, Y.Y.[Yan-Yang], Tang, Z.M.[Zhen-Min], Ding, Y.H.[Yu-Hua],
Double Constrained NMF for Partial Multi-View Clustering,
DICTA16(1-7)
IEEE DOI 1701
nonnegative matrix factorization. Clustering methods BibRef

Hedhli, I.[Ihsen], Moser, G.[Gabriele], Serpico, S.B.[Sebastiano B.], Zerubia, J.B.[Josiane B.],
Contextual multi-scale image classification on quadtree,
ICIP16(1349-1353)
IEEE DOI 1610
Analytical models BibRef

Romero, A.R., Jayawardena, S., Cox, M., Borges, P.V.K.,
Partitioning the Input Domain for Classification,
DICTA15(1-8)
IEEE DOI 1603
computational complexity BibRef

Villalon-Turrubiates, I.E.,
A dynamical model to classify the content of multitemporal images employing distributed computing techniques,
MultiTemp15(1-4)
IEEE DOI 1511
Big Data BibRef

Febrer-Hernández, J.K.[José K.], Hernández-León, R.[Raudel], Hernández-Palancarr, J.[José], Feregrino-Uribe, C.[Claudia],
Improving the Accuracy of the Sequential Patterns-Based Classifiers,
CIARP15(708-715).
Springer DOI 1511
BibRef

Aroche-Villarruel, A.A.[Argenis A.], Martínez-Trinidad, J.F.[José F.], Carrasco-Ochoa, J.A.[Jesús Ariel], Pérez-Suárez, A.[Airel],
A Different Approach for Pruning Micro-clusters in Data Stream Clustering,
MCPR15(33-43).
Springer DOI 1506
BibRef

Choi, K.S.[Kang-Sun], Oh, K.W.[Ki-Won],
Fast Simple Linear Iterative Clustering by Early Candidate Cluster Elimination,
IbPRIA15(579-586).
Springer DOI 1506
BibRef

Mustafa, W.[Wail], Kraft, D.[Dirk], Krüger, N.[Norbert],
Extracting Categories by Hierarchical Clustering Using Global Relational Features,
IbPRIA15(541-551).
Springer DOI 1506
BibRef

Ali, M.[Mohsen], Ho, J.[Jeffrey],
Deconstructing Binary Classifiers in Computer Vision,
ACCV14(III: 468-482).
Springer DOI 1504
BibRef

Tax, D.M.J.[David M.J.], Sontrop, H.M.J.[Herman M.J.], Reinders, M.J.T.[Marcel J.T], Moerland, P.D.[Perry D.],
The Effect of Aggregating Subtype Performances Depends Strongly on the Performance Measure Used,
ICPR14(3720-3725)
IEEE DOI 1412
Accuracy BibRef

Zemene, E.[Eyasu], Alemu, L.T., Pelillo, M.[Marcello],
Constrained dominant sets for retrieval,
ICPR16(2568-2573)
IEEE DOI 1705
Coherence, Databases, Diffusion processes, Face, Manifolds, Optimization BibRef

Zemene, E.[Eyasu], Pelillo, M.[Marcello],
Path-Based Dominant-Set Clustering,
CIAP15(I:150-160).
Springer DOI 1511
BibRef

Biggio, B.[Battista], Bulň, S.R.[Samuel Rota], Pillai, I.[Ignazio], Mura, M.[Michele], Zemene Mequanint, E.[Eyasu], Pelillo, M.[Marcello], Roli, F.[Fabio],
Poisoning Complete-Linkage Hierarchical Clustering,
SSSPR14(42-52).
Springer DOI 1408
preventing deliberate attack on clustering algorithm in security application. BibRef

Chandrashekar, V., Kumar, S., Jawahar, C.V.,
Compacting Large and Loose Communities,
ACPR13(522-526)
IEEE DOI 1408
eigenvalues and eigenfunctions BibRef

Rosales-Méndez, H.[Henry], Ramírez-Cruz, Y.[Yunior],
CICE-BCubed: A New Evaluation Measure for Overlapping Clustering Algorithms,
CIARP13(I:157-164).
Springer DOI 1311
BibRef

Li, X.[Xin], Guo, Y.H.[Yu-Hong],
Multi-level Adaptive Active Learning for Scene Classification,
ECCV14(VII: 234-249).
Springer DOI 1408
BibRef
Earlier:
Adaptive Active Learning for Image Classification,
CVPR13(859-866)
IEEE DOI 1309
BibRef
Earlier:
An Object Co-occurrence Assisted Hierarchical Model for Scene Understanding,
BMVC12(81).
DOI Link 1301
active learning; image classification BibRef

Yenialp, E.[Erdal], Kalkan, H.[Habil], Mete, M.[Mutlu],
Improving Density Based Clustering with Multi-Scale Analysis,
ICCVG12(694-701).
Springer DOI 1210
BibRef

Pauly, O.[Olivier], Mateus, D.[Diana], Navab, N.[Nassir],
STARS: A new ensemble partitioning approach,
ITCVPR11(1340-1347).
IEEE DOI 1201
Several Thresholds on a Random Subspace. BibRef

Zhou, Y.[Yujin], Tan, Y.H.[Yi-Hua], Li, H.T.[Hai-Tao], Gu, H.[Haiyan],
A Multi-Classifier Combined Decision Tree Hierarchical Classification Method,
ISIDF11(1-3).
IEEE DOI 1111
BibRef

Dikmen, M.[Mert], Huang, T.S.[Thomas S.],
Improving Classification Accuracy by Comparing Local Features through Canonical Correlations,
ICPR10(4032-4035).
IEEE DOI 1008
BibRef

Strange, H.[Harry], Zwiggelaar, R.[Reyer],
Iterative Hyperplane Merging: A Framework for Manifold Learning,
BMVC10(xx-yy).
HTML Version. 1009
BibRef

Cho, M.[Minsu], Kyoung, M.L.[Mu-Lee],
Authority-shift clustering: Hierarchical clustering by authority seeking on graphs,
CVPR10(3193-3200).
IEEE DOI 1006
BibRef

Joshi, M.[Manish], Lingras, P.[Pawan],
Evolutionary and Iterative Crisp and Rough Clustering I: Theory,
PReMI09(615-620).
Springer DOI 0912
BibRef

Joshi, M.[Manish], Lingras, P.[Pawan],
Evolutionary and Iterative Crisp and Rough Clustering II: Experiments,
PReMI09(621-627).
Springer DOI 0912
BibRef

Ma, H.B.[Hong-Bin], Zhang, C.[Cun], Yang, S.F.[Sheng-Fei], Xu, J.F.[Jun-Fang],
Object Oriented Information Extraction from High Resolution Remote Sensing Imagery,
CISP09(1-5).
IEEE DOI 0910
BibRef

Xu, J.W.[Jian-Wu], Singh, V.[Vartika], Govindaraju, V.[Venu], Neogi, D.[Depankar],
A Hierarchical Classification Model for Document Categorization,
ICDAR09(486-490).
IEEE DOI 0907
BibRef

Sledge, I.J.[Isaac J.], Keller, J.M.[James M.],
Growing neural gas for temporal clustering,
ICPR08(1-4).
IEEE DOI 0812
Genetic Algorithm for clusters BibRef

Kuncheva, L.I.[Ludmila I.], Plumpton, C.O.[Catrin O.],
Adaptive Learning Rate for Online Linear Discriminant Classifiers,
SSPR08(510-519).
Springer DOI 0812
BibRef

Kuncheva, L.I.[Ludmila I.], Zliobaite, I.[Indre],
Linear Discriminant Classifier (LDC) for Streaming Data with Concept Drift,
SSPR08(4).
Springer DOI 0812
Trained using latest N observations. BibRef

Elghazel, H.[Haytham], Hacid, M.S.[Mohand-Said],
Aggregated Search in Graph Databases: Preliminary Results,
GbRPR11(92-101).
Springer DOI 1105
BibRef

Elghazel, H.[Haytham], Yoshida, T.[Tetsuya], Deslandres, V.[Véronique], Hacid, M.S.[Mohand-Said], Dussauchoy, A.[Alain],
A New Greedy Algorithm for Improving b-Coloring Clustering,
GbRPR07(228-239).
Springer DOI 0706
BibRef

Aghagolzadeh, M., Soltanian-Zadeh, H., Araabi, B., Aghagolzadeh, A.,
A Hierarchical Clustering Based on Mutual Information Maximization,
ICIP07(I: 277-280).
IEEE DOI 0709
BibRef

Roth, V.[Volker], Fischer, B.[Bernd],
The kernelHMM: Learning Kernel Combinations in Structured Output Domains,
DAGM07(436-445).
Springer DOI 0709
BibRef

Sakai, T.[Tomoya], Imiya, A.[Atsushi],
Validation of Watershed Regions by Scale-Space Statistics,
SSVM09(175-186).
Springer DOI 0906
BibRef

Sakai, T.[Tomoya], Imiya, A.[Atsushi],
Statistically Valid Graph Representations of Scale-Space Geometry,
ICISP08(338-345).
Springer DOI 0807
BibRef

Sakai, T.[Tomoya],
Multiple pattern classification by sparse subspace decomposition,
Subspace09(170-177).
IEEE DOI 0910
BibRef

Sakai, T.[Tomoya],
Monte Carlo subspace method: An incremental approach to high-dimensional data classification,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Sakai, T.[Tomoya], Komazaki, T.[Takuto], Imiya, A.[Atsushi],
Scale-Space Clustering with Recursive Validation,
SSVM07(288-299).
Springer DOI 0705
BibRef

Karadag, O.O.[Ozge Oztimur], Vural, F.T.Y.[Fatos T. Yarman],
HANOLISTIC: A Hierarchical Automatic Image Annotation System Using Holistic Approach,
VCL-ViSU09(16-21).
IEEE DOI 0906
BibRef

Akbas, E.[Emre], Vural, F.T.Y.[Fatos T. Yarman],
Automatic Image Annotation by Ensemble of Visual Descriptors,
SLAM07(1-8).
IEEE DOI 0706
Do not just add all the features, due to different types, redundancy. Each feature learned at lowest level, combined at other levels. BibRef

Zhao, L.[Liyue], Sukthankar, G.[Gita], Sukthankar, R.[Rahul],
Importance-weighted label prediction for active learning with noisy annotations,
ICPR12(3476-3479).
WWW Link. 1302
BibRef

Yang, L.[Liu], Jin, R.[Rong], Pantofaru, C.[Caroline], Sukthankar, R.[Rahul],
Discriminative Cluster Refinement: Improving Object Category Recognition Given Limited Training Data,
CVPR07(1-8).
IEEE DOI 0706
BibRef

Pantofaru, C., Hebert, M.,
A framework for learning to recognize and segment object classes using weakly supervised training data,
BMVC07(xx-yy).
PDF File. 0709
BibRef

Monteleoni, C.[Claire], Kaariainen, M.[Matti],
Practical Online Active Learning for Classification,
Learning07(1-8).
IEEE DOI 0706
BibRef

Gagrani, A.[Aakanksha], Gupta, L.[Lalit], Ravindran, B., Das, S.[Sukhendu], Roychowdhury, P.[Pinaki], Panchal, V.K.,
A Hierarchical Approach to Landform Classification of Satellite Images Using a Fusion Strategy,
ICCVGIP06(140-151).
Springer DOI 0612
First into 3 major landforms, then classify within each of these. BibRef

Sternby, J.[Jakob],
Class Dependent Cluster Refinement,
ICPR06(II: 833-836).
IEEE DOI 0609
BibRef

Lam, B.S.Y.[Benson S. Y.], Yan, H.[Hong],
Improved Clustering Algorithm Based on Calculus of Variation,
ICPR06(I: 900-903).
IEEE DOI 0609
BibRef

Prehn, H.[Herward], Sommer, G.[Gerald],
An Adaptive Classification Algorithm Using Robust Incremental Clustering,
ICPR06(I: 896-899).
IEEE DOI 0609
BibRef

Azran, A.[Arik], Ghahramani, Z.[Zoubin],
Spectral Methods for Automatic Multiscale Data Clustering,
CVPR06(I: 190-197).
IEEE DOI 0606
BibRef

Zhang, K.[Kai], Tang, M.[Ming], Kwok, J.T.[James T.],
Applying Neighborhood Consistency for Fast Clustering and Kernel Density Estimation,
CVPR05(II: 1001-1007).
IEEE DOI 0507
BibRef

Carrivick, L.[Luke], Prabhu, S.[Sanjay], Goddard, P.[Paul], Rossiter, J.[Jonathan],
Unsupervised Learning in Radiology Using Novel Latent Variable Models,
CVPR05(II: 854-859).
IEEE DOI 0507
BibRef

Bouvrie, J.V.[Jake V.],
Multiple Resolution Image Classification,
MIT AIMAIM-2002-022, December 2002.
WWW Link. In this paper we evaluate a selection of popular techniques in an effort to find a feature set/ classifier combination which generalizes well to full resolution image data. 0306
BibRef

Shi, S.M.[Shu-Ming], Yang, G.W.[Guang-Wen], Wang, D.X.[Ding-Xing], Zheng, W.M.[Wei-Min],
Potential-based hierarchical clustering,
ICPR02(IV: 272-275).
IEEE DOI 0211
BibRef

Rendon, E., Barandela, R.,
Fast hierarchical clustering based on compressed data,
ICPR02(II: 216-219).
IEEE DOI 0211
BibRef

Zöller, T., Buhmann, J.M.,
Active Learning for Hierarchical Pairwise Data Clustering,
ICPR00(Vol II: 186-189).
IEEE DOI 0009
BibRef

Chardin, A., Perez, P.,
Unsupervised Image Classification with a Hierarchical EM Algorithm,
ICCV99(969-974).
IEEE DOI BibRef 9900
Earlier:
Semi-iterative inference with hierarchical models,
ICIP98(I: 630-634).
IEEE DOI 9810
BibRef

Schikuta, E.,
Grid-Clustering: An Efficient Hierarchical Clustering Method for Very Large Data Sets,
ICPR96(II: 101-105).
IEEE DOI 9608
(Univ. of Vienna, A) BibRef

Bajcsy, P., Ahuja, N.,
Uniformity and Homogeneity Based Hierarchical Clustering,
ICPR96(II: 96-100).
IEEE DOI 9608
(Univ. of Illinois, Urbana, USA) BibRef

Roberts, S.J.,
Scale-Space Unsupervised Cluster Analysis,
ICPR96(II: 106-110).
IEEE DOI 9608
(Univ. of London, UK) BibRef

Jin, J.S.,
Hierarchical pattern matching using a high entropy signature,
ICPR94(B:436-438).
IEEE DOI 9410
BibRef

Prabhu, S.M., Garg, D.P., Spano, Sr., M.R.,
A hierarchical labeled object classification system,
ICPR94(B:479-481).
IEEE DOI 9410
BibRef

Jiang, H.T.[Hong-Tao], Bolviken, E.,
A general parameter updating approach to image classification,
ICPR94(A:720-722).
IEEE DOI 9410
BibRef

Tseng, C.T., Moret, B.M.E.,
The design of a nonparametric hierarchical classifier,
ICPR90(I: 428-432).
IEEE DOI 9006
BibRef

Li, X.,
Hierarchical clustering on SIMD machines with alignment network,
CVPR89(660-665).
IEEE DOI 0403
BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Subspace Clustering .


Last update:Nov 18, 2017 at 20:56:18