14.1.5.2.2 Hierarchical Combination, Multi-Stage Classifiers

Chapter Contents (Back)
Combination. Cascade Classifier. Classifer Combinations.

Takiyama, R.[Ryuzo],
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Architectures for Detecting and Solving Conflicts: Two-Stage Classification and Support Vector Classifiers,
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Earlier: A3, A1, A2:
An overview and comparison of voting methods for pattern recognition,
FHR02(195-200).
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Quost, B.[Benjamin], Denoeux, T.[Thierry], Masson, M.H.[Marie-Helene],
Pairwise classifier combination using belief functions,
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Polychotomous classification; Dempster-Shafer theory; Evidence theory; Classification; Classifier fusion BibRef

Ko, A.H.R.[Albert Hung-Ren], Sabourin, Jr., R.[Robert], de Souza Britto, A.[Alceu], Soares de Oliveira, L.E.[Luiz E.],
Pairwise fusion matrix for combining classifiers,
PR(40), No. 8, August 2007, pp. 2198-2210.
WWW Link. 0704
BibRef
Earlier: A1, A2, A3, Only:
A New Objective Function for Ensemble Selection in Random Subspaces,
ICPR06(IV: 185-188).
IEEE DOI 0609
Fusion function; Combining classifiers; Confusion matrix; Pattern recognition; Majority voting; Ensemble of learning machines BibRef

Ko, A.H.R.[Albert Hung-Ren], Sabourin, Jr., R.[Robert], de Souza Britto, A.[Alceu],
From dynamic classifier selection to dynamic ensemble selection,
PR(41), No. 5, May 2008, pp. 1735-1748.
WWW Link. 0711
BibRef
Earlier:
K-Nearest Oracle for Dynamic Ensemble Selection,
ICDAR07(422-426).
IEEE DOI 0709
Oracle; Combining classifiers; Classifier selection; Ensemble selection; Pattern recognition; Majority voting; Ensemble of learning machines BibRef

dos Santos, E.M.[Eulanda M.], Sabourin, R.[Robert], Maupin, P.[Patrick],
A dynamic overproduce-and-choose strategy for the selection of classifier ensembles,
PR(41), No. 10, October 2008, pp. 2993-3009.
WWW Link. 0808
Overproduce-and-choose strategy; Dynamic classifier selection; Optimization; Measures of confidence BibRef

Ko, A.H.R.[Albert Hung-Ren], Sabourin, Jr., R.[Robert], Soares de Oliveira, L.E.[Luiz E.], de Souza Britto, A.[Alceu],
The implication of data diversity for a classifier-free ensemble selection in random subspaces,
ICPR08(1-5).
IEEE DOI 0812
BibRef

Ayad, H.G.[Hanan G.], Kamel, M.S.[Mohamed S.],
Cumulative Voting Consensus Method for Partitions with Variable Number of Clusters,
PAMI(30), No. 1, January 2008, pp. 160-173.
IEEE DOI 0711
BibRef

Ayad, H.G.[Hanan G.], Kamel, M.S.[Mohamed S.],
On voting-based consensus of cluster ensembles,
PR(43), No. 5, May 2010, pp. 1943-1953.
Elsevier DOI 1003
Clustering; Cluster ensembles; Voting-based consensus BibRef

Wu, J.X.[Jian-Xin], Brubaker, S.C.[S. Charles], Mullin, M.D.[Matthew D.], Rehg, J.M.[James M.],
Fast Asymmetric Learning for Cascade Face Detection,
PAMI(30), No. 3, March 2008, pp. 369-382.
IEEE DOI 0801
Face Detection. Separate feature selection and classifier ensemble formation. BibRef

Brubaker, S.C.[S. Charles], Mullin, M.D.[Matthew D.], Rehg, J.M.[James M.],
Towards Optimal Training of Cascaded Detectors,
ECCV06(I: 325-337).
Springer DOI 0608
Face recognition. Analysis of the technique. BibRef

Brubaker, S.C.[S. Charles], Wu, J.X.[Jian-Xin], Sun, J.[Jie], Mullin, M.D.[Matthew D.], Rehg, J.M.[James M.],
Towards the Optimal Training of Cascades of Boosted Ensembles,

On the Design of Cascades of Boosted Ensembles for Face Detection,
IJCV(77), No. 1-3, May 2008, pp. 65-86.
Springer DOI 0803
BibRef
Earlier: CLOR06(301-320).
Springer DOI 0711
BibRef

Hore, P.[Prodip], Hall, L.O.[Lawrence O.], Goldgof, D.B.[Dmitry B.],
A scalable framework for cluster ensembles,
PR(42), No. 5, May 2009, pp. 676-688.
Elsevier DOI 0902
Clustering; Hard/fuzzy-k-means; Large data sets; Ensemble; Scalability; Single pass algorithm See also generic knowledge-guided image segmentation and labeling system using fuzzy clustering algorithms, A. BibRef

Rodriguez, J.J.[Juan J.], Garcia-Osorio, C.[Cesar], Maudes, J.[Jesus],
Forests of nested dichotomies,
PRL(31), No. 2, 15 January 2010, pp. 125-132.
Elsevier DOI 1001
Nested dichotomies; Classifier ensembles; Multiclass classification; Decision trees BibRef

Foo, B., van der Schaar, M.,
A Distributed Approach for Optimizing Cascaded Classifier Topologies in Real-Time Stream Mining Systems,
IP(19), No. 11, November 2010, pp. 3035-3048.
IEEE DOI 1011
Configure classifiers in real-time. BibRef

Hullermeier, E.[Eyke], Vanderlooy, S.[Stijn],
Combining predictions in pairwise classification: An optimal adaptive voting strategy and its relation to weighted voting,
PR(43), No. 1, January 2010, pp. 128-142.
Elsevier DOI 0909
Learning by pairwise comparison; Label ranking; Aggregation strategies; Classifier combination; Weighted voting; MAP prediction BibRef

Galar, M.[Mikel], Fernandez, A.[Alberto], Barrenechea, E.[Edurne], Bustince, H.[Humberto], Herrera, F.[Francisco],
An overview of ensemble methods for binary classifiers in multi-class problems: Experimental study on one-vs-one and one-vs-all schemes,
PR(44), No. 8, August 2011, pp. 1761-1776.
Elsevier DOI 1104
Survey, Ensemble Clustering. Multi-classification; Pairwise learning; One-vs-one; One-vs-all; Decomposition strategies; Ensembles BibRef

Galar, M.[Mikel], Fernández, A.[Alberto], Barrenechea, E.[Edurne], Bustince, H.[Humberto], Herrera, F.[Francisco],
Dynamic classifier selection for One-vs-One strategy: Avoiding non-competent classifiers,
PR(46), No. 12, 2013, pp. 3412-3424.
Elsevier DOI 1308
Multi-classification See also EUSBoost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling. BibRef

Galar, M.[Mikel], Fernández, A.[Alberto], Barrenechea, E.[Edurne], Herrera, F.[Francisco],
DRCW-OVO: Distance-based relative competence weighting combination for One-vs-One strategy in multi-class problems,
PR(48), No. 1, 2015, pp. 28-42.
Elsevier DOI 1410
Multi-class classification BibRef

Foo, B., Turaga, D.S., Verscheure, O., van der Schaar, M., Amini, L.,
Configuring Trees of Classifiers in Distributed Multimedia Stream Mining Systems,
CirSysVideo(21), No. 3, March 2011, pp. 245-258.
IEEE DOI 1104
BibRef

Visentini, I.[Ingrid], Snidaro, L.[Lauro], Foresti, G.L.[Gian Luca],
Cascaded online boosting,
RealTimeIP(5), No. 4, December 2010, pp. 245-257.
WWW Link. 1101
BibRef
Earlier:
On-line boosted cascade for object detection,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Feitosa, R.Q.[Raul Queiroz], da Costa, G.A.O.P.[Gilson Alexandre Ostwald Pedro], Mota, G.L.A.[Guilherme Lucio Abelha], Feijo, B.[Bruno],
Modeling alternatives for fuzzy Markov chain-based classification of multitemporal remote sensing data,
PRL(32), No. 7, 1 May 2011, pp. 927-940.
Elsevier DOI 1101
Classification; Multitemporal image analysis; Fuzzy Markov chain BibRef

Zhang, C.X.[Chun-Xia], Duin, R.P.W.[Robert P.W.],
An experimental study of one- and two-level classifier fusion for different sample sizes,
PRL(32), No. 14, 15 October 2011, pp. 1756-1767.
Elsevier DOI 1110
Ensemble classifier; Classifier fusion rule; Training sample size; Fixed combiner; Trainable combiner BibRef

Tu, Q., Lu, J.F., Yuan, B., Tang, J.B., Yang, J.Y.,
Density-based hierarchical clustering for streaming data,
PRL(33), No. 5, 1 April 2012, pp. 641-645.
Elsevier DOI 1202
Streaming data; Density-based clustering; Hierarchical method BibRef

Sarmah, S.[Sauravjyoti], Bhattacharyya, D.K.[Dhruba K.],
A grid-density based technique for finding clusters in satellite image,
PRL(33), No. 5, 1 April 2012, pp. 589-604.
Elsevier DOI 1202
Clustering; Grid; Density; High resolution; High dimensional satellite images; Gabor wavelets. Clusters are very different sizes. BibRef

Li, Y.[Yan], Tax, D.M.J.[David M.J.], Duin, R.P.W.[Robert P.W.], Loog, M.[Marco],
Multiple-instance learning as a classifier combining problem,
PR(46), No. 3, March 2013, pp. 865-874.
Elsevier DOI 1212
Multiple instance learning; Classifier combining BibRef

Cheplygina, V.[Veronika], Tax, D.M.J.[David M.J.], Loog, M.[Marco],
Does one rotten apple spoil the whole barrel?,
ICPR12(1156-1159).
WWW Link. 1302
Multiple Instance Learning. BibRef

Loog, M.[Marco], Yang, Y.,
An empirical investigation into the inconsistency of sequential active learning,
ICPR16(210-215)
IEEE DOI 1705
Convergence, Learning systems, Logistics, Loss measurement, Pattern recognition, Standards, Training BibRef

Susnjak, T.[Teo], Barczak, A.[Andre], Reyes, N.[Napoleon], Hawick, K.[Ken],
Coarse-to-fine multiclass learning and classification for time-critical domains,
PRL(34), No. 8, June 2013, pp. 884-894.
Elsevier DOI 1305
BibRef
Earlier:
A New Ensemble-Based Cascaded Framework for Multiclass Training with Simple Weak Learners,
CAIP11(I: 563-570).
Springer DOI 1109
Coarse-to-fine learning; Multiclass classification; Classifier ensembles; Boosting; Classifier cascades; Training runtime constraints BibRef

Li, N.[Nan], Tsang, I.W.H.[Ivor W.H.], Zhou, Z.H.[Zhi-Hua],
Efficient Optimization of Performance Measures by Classifier Adaptation,
PAMI(35), No. 6, June 2013, pp. 1370-1382.
IEEE DOI 1305
First train non-linear classifiers, then adapt by optimizing performance measures. BibRef

Mao, Q.[Qi], Tsang, I.W.H.[Ivor Wai-Hung],
A Feature Selection Method for Multivariate Performance Measures,
PAMI(35), No. 9, 2013, pp. 2051-2063.
IEEE DOI 1307
Convergence. Optimize multi-variate measures, not just classification error. BibRef

Bouges, P.[Pierre], Chateau, T.[Thierry], Blanc, C.[Christophe], Loosli, G.[Gaëlle],
Handling missing weak classifiers in boosted cascade: application to multiview and occluded face detection,
JIVP(2013), No. 1, 2013, pp. 55.
DOI Link 1311
BibRef
Earlier:
Using k-nearest neighbors to handle missing weak classifiers in a boosted cascade,
ICPR12(1763-1766).
WWW Link. 1302
BibRef

Ludwig, O., Nunes, U., Ribeiro, B., Premebida, C.,
Improving the Generalization Capacity of Cascade Classifiers,
Cyber(43), No. 6, 2013, pp. 2135-2146.
IEEE DOI 1312
feature extraction BibRef

Li, Y.[Yali], Wang, S.J.[Sheng-Jin], Tian, Q.[Qi], Ding, X.Q.[Xiao-Qing],
Learning Cascaded Shared-Boost Classifiers for Part-Based Object Detection,
IP(23), No. 4, April 2014, pp. 1858-1871.
IEEE DOI 1404
image representation BibRef

Derrode, S.[Stéphane], Benyoussef, L.[Lamia], Pieczynski, W.[Wojciech],
Subsampling-based HMC parameter estimation with application to large datasets classification,
SIViP(8), No. 5, July 2014, pp. 873-882.
Springer DOI 1407
Hidden Markov chain models. BibRef

Mansouri, J.[Jafar], Khademi, M.[Morteza],
Tree Fusion Method for Semantic Concept Detection in Images,
IEICE(E97-D), No. 8, August 2014, pp. 2209-2211.
WWW Link. 1408
semantic concept detection. BibRef

Hedhli, I.[Ihsen], Moser, G.[Gabriele], Zerubia, J.B.[Josiane B.], Serpico, S.B.[Sebastiano B.],
A New Cascade Model for the Hierarchical Joint Classification of Multitemporal and Multiresolution Remote Sensing Data,
GeoRS(54), No. 11, November 2016, pp. 6333-6348.
IEEE DOI 1610
BibRef
Earlier:
New cascade model for hierarchical joint classification of multitemporal, multiresolution and multisensor remote sensing data,
ICIP14(5247-5251
IEEE DOI 1502
Data models BibRef


Qiu, Q.A.[Qi-Ang], Sapiro, G.[Guillermo],
Learning Transformations,
ICIP14(4008-4012)
IEEE DOI 1502
Accuracy BibRef

Torres-Pereira, E.[Eanes], Martins-Gomes, H.[Herman], Monteiro-Brito, A.E.[Andrey Elísio], de Carvalho, J.M.[João Marques],
Hybrid Parallel Cascade Classifier Training for Object Detection,
CIARP14(810-817).
Springer DOI 1411
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Chen, B.[Bo], Perona, P.[Pietro], Bourdev, L.[Lubomir],
Hierarchical Cascade of Classifiers for Efficient Poselet Evaluation,
BMVC14(xx-yy).
HTML Version. 1410
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Weiss, D.[David], Sapp, B.[Benjamin], Taskar, B.[Ben],
Dynamic Structured Model Selection,
ICCV13(2656-2663)
IEEE DOI 1403
pose estimation; structured prediction BibRef

Marcialis, G.L.[Gian Luca], Didaci, L.[Luca], Roli, F.[Fabio],
Estimating the Serial Combination's Performance from That of Individual Base Classifiers,
CIAP13(I:622-631).
Springer DOI 1311
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Sznitman, R.[Raphael], Becker, C.[Carlos], Fleuret, F.[Francois], Fua, P.[Pascal],
Fast Object Detection with Entropy-Driven Evaluation,
CVPR13(3270-3277)
IEEE DOI 1309
Computer Vision. Speedup cascade style classifier combination. BibRef

Yamasaki, T.[Toshihiko], Chen, T.H.[Tsu-Han],
Confidence-assisted classification result refinement for object recognition featuring TopN-Exemplar-SVM,
ICPR12(1783-1786).
WWW Link. 1302
Classifier cascade BibRef

Chen, Y.T.[Yu-Tian], Gelfand, A.[Andrew], Fowlkes, C.C.[Charless C.], Welling, M.[Max],
Integrating local classifiers through nonlinear dynamics on label graphs with an application to image segmentation,
ICCV11(2635-2642).
IEEE DOI 1201
Combine locally trained models into globel model. BibRef

Parvin, H.[Hamid], Minaei-Bidgoli, B.[Behrouz], Parvin, S.[Sajad],
A Scalable Heuristic Classifier for Huge Datasets: A Theoretical Approach,
CIARP11(380-390).
Springer DOI 1111
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Parvin, H.[Hamid], Minaei-Bidgoli, B.[Behrouz], Parvin, S.[Sajad],
An Accumulative Points/Votes Based Approach for Feature Selection,
CIARP11(399-408).
Springer DOI 1111
BibRef

Jain, V.[Vidit], Learned-Miller, E.G.[Erik G.],
Online domain adaptation of a pre-trained cascade of classifiers,
CVPR11(577-584).
IEEE DOI 1106
BibRef

Preet, P., Chowdhury, P.R., Malik, G.S.,
Correlation based object-specific attentional mechanism for target localization in high resolution satellite images,
NCVPRIPG13(1-4)
IEEE DOI 1408
geophysical image processing BibRef

Mangai, U.G., Samanta, S., Das, S., Chowdhury, P.R., Varghese, K., Kalra, M.,
A Hierarchical Multi-classifier Framework for Landform Segmentation Using Multi-spectral Satellite Images: A Case Study over the Indian Subcontinent,
PSIVT10(306-313).
IEEE DOI 1011
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Wang, P.[Peng], Shen, C.H.[Chun-Hua], Zheng, H.[Hong], Ren, Z.[Zhang],
Training a multi-exit cascade with linear asymmetric classification for efficient object detection,
ICIP10(61-64).
IEEE DOI 1009
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Day, M.[Matthew], Robinson, J.A.[John A.],
Constructing efficient cascade classifiers for object detection,
ICIP10(3781-3784).
IEEE DOI 1009
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Cordella, L.P.[Luigi P.], de Stefano, C.[Claudio], Fontanella, F.[Francesco], Marrocco, C.[Cristina], di Freca, A.S.[Alessandra Scotto],
Combining Single Class Features for Improving Performance of a Two Stage Classifier,
ICPR10(4352-4355).
IEEE DOI 1008
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Szczot, M.[Magdalena], Forster, J.[Julian], Lohlein, O.[Otto], Palm, G.[Gunther],
Package Boosting for Readaption of Cascaded Classifiers,
ICPR10(552-555).
IEEE DOI 1008
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Parakhin, M.[Mikhail], Haluptzok, P.[Patrick],
Finding the Most Probable Ranking of Objects with Probabilistic Pairwise Preferences,
ICDAR09(616-620).
IEEE DOI 0907
Ranking when pairwise ranking is inconsistent (not transitive). apply to handwriting. BibRef

Zhang, X.Q.[Xu-Qing], Wu, F.[Fei], Zhuang, Y.T.[Yue-Ting],
Clustering by evidence accumulation on affinity propagation,
ICPR08(1-4).
IEEE DOI 0812
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Kukenys, I.[Ignas], Browne, W.N.[Will N.], Zhang, M.J.[Meng-Jie],
Transparent, Online Image Pattern Classification Using a Learning Classifier System,
EvoIASP11(183-193).
Springer DOI 1104
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Kukenys, I.[Ignas], McCane, B.[Brendan], Neumegen, T.[Tim],
Training Support Vector Machines on Large Sets of Image Data,
ACCV09(III: 331-340).
Springer DOI 0909
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Kukenys, I.[Ignas], McCane, B.[Brendan],
Classifier cascades for support vector machines,
IVCNZ08(1-6).
IEEE DOI 0811
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Mirzaei, A.[Abdolreza], Rahmati, M.[Mohammad],
Combining hierarchical clusterings using min-transitive closure,
ICPR08(1-4).
IEEE DOI 0812
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El-Sherif, E.[Ezzat], Abdelazeem, S.[Sherif], El-Yazeed, M.F.A.[M. Fathy Abu],
Automatic generation of optimum classification cascades,
ICPR08(1-4).
IEEE DOI 0812
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Concepción Morales, E.R.[Eduardo R.], Yurramendi Mendizabal, Y.[Yosu],
Building and Assessing a Constrained Clustering Hierarchical Algorithm,
CIARP08(211-218).
Springer DOI 0809
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Ranzato, M.[Marc'Aurelio], Hinton, G.E.[Geoffrey E.],
Modeling pixel means and covariances using factorized third-order boltzmann machines,
CVPR10(2551-2558).
IEEE DOI 1006
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Kavukcuoglu, K.[Koray], Ranzato, M.[Marc'Aurelio], Fergus, R.[Rob], Le Cun, Y.L.[Yann L.],
Learning invariant features through topographic filter maps,
CVPR09(1605-1612).
IEEE DOI 0906
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Boureau, Y.L.[Y-Lan], Le Roux, N.[Nicolas], Bach, F.[Francis], Ponce, J.[Jean], Le Cun, Y.L.[Yann L.],
Ask the locals: Multi-way local pooling for image recognition,
ICCV11(2651-2658).
IEEE DOI 1201
Pooling feature vectors over neighborhoods is not local in feature space. Apply to feature space also. BibRef

Boureau, Y.L.[Y-Lan], Bach, F.[Francis], Le Cun, Y.L.[Yann L.], Ponce, J.[Jean],
Learning mid-level features for recognition,
CVPR10(2559-2566).
IEEE DOI 1006
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Ranzato, M.[Marc'Aurelio], Huang, F.J.[Fu Jie], Boureau, Y.L.[Y-Lan], Le Cun, Y.L.[Yann L.],
Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition,
CVPR07(1-8).
IEEE DOI 0706
Hierarchical representation. Learn on features, then on patches of features from first level. BibRef

Dundar, M.M.[M. Murat], Bi, J.B.[Jin-Bo],
Joint Optimization of Cascaded Classifiers for Computer Aided Detection,
CVPR07(1-8).
IEEE DOI 0706
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Chen, H.X.[Hai-Xia], Yuan, S.[Senmiao], Jiang, K.[Kai],
Adaptive Classifier Selection Based on Two Level Hypothesis Tests for Incremental Learning,
SSPR06(687-695).
Springer DOI 0608
BibRef

Luo, H.T.[Hui-Tao],
Optimization Design of Cascaded Classifiers,
CVPR05(I: 480-485).
IEEE DOI 0507
BibRef

Hamamura, T., Mizutani, H., Irie, B.,
A multiclass classification method based on multiple pairwise classifiers,
ICDAR03(809-813).
IEEE DOI 0311
BibRef

Heiseleyz, B.[Bernd], Serrey, T.[Thomas], Mukherjeey, S.[Sayan], Poggio, T.[Tomaso],
Feature Reduction and Hierarchy of Classifiers for Fast Object Detection in Video Images,
CVPR01(II:18-24).
IEEE DOI 0110
Speed up object detection using SVM classifiers. Hierarchy with many selected first, then more accurate. BibRef

Chou, Y.Y., Shapiro, L.G.,
A Hierarchical Multiple Classifier Learning Algorithm,
ICPR00(Vol II: 152-155).
IEEE DOI 0009
BibRef

Sun, F., Omachi, S., Kato, N., Aso, H., Kono, S., Takagi, T.,
Two-stage Computational Cost Reduction Algorithm Based on Mahalanobis Distance Approximations,
ICPR00(Vol II: 696-699).
IEEE DOI 0009
BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Classifier Combination, Evaluation, Overview, Appliction Specific .


Last update:Sep 25, 2017 at 16:36:46