14.1.14.3 Decision Fusion

Stephanou, H.E., and Lu, S.Y.,
Measuring Consensus Effectiveness by a Generalized Entropy Criterion,
PAMI(10), No. 4, July 1988, pp. 544-554.
IEEE DOI BibRef 8807

Ho, T.K., Hull, J.J., Srihari, S.N.,
Decision Combination in Multiple Classifier Systems,
PAMI(16), No. 1, January 1994, pp. 66-75.
IEEE DOI BibRef 9401
Earlier:
On multiple classifier systems for pattern recognition,
ICPR92(II:84-87).
IEEE DOI 9208
The Borda count. Unanimous consensus for selection. BibRef

Dasarathy, B.V.,
Fusion Strategies for Enhancing Decision Reliability in Multisensor Environments,
OptEng(35), No. 3, March 1996, pp. 603-616. BibRef 9603

Dasarathy, B.V.,
Sensor Fusion Potential Exploitation: Innovative Architectures and Illustrative Applications,
PIEEE(85), No. 1, January 1997, pp. 24-38. 9701
BibRef

Dasarathy, B.V.,
Adaptive Fusion Processor Paradigms for Fusion of Information Acquired at Different Levels of Detail,
OptEng(35), No. 3, March 1996, pp. 634-649. BibRef 9603

Dasarathy, B.V.,
Asymmetric Fusion strategies for target detection in multisensor environments,
SPIE(3067), April 1997, pp. 26-37. BibRef 9704

Dasarathy, B.V.,
Decision Fusion,
ISBN 0-8186-4452-4, IEEE Computer Society PressLos Alamitos, CA, 1994. BibRef 9400

Dasarathy, B.V.,
Decision Fusion Strategies for Target Detection with a Three-Sensor Suite,
SPIE(3067), April 1997, pp. 14-25. BibRef 9704

Rao, N.S.V., Iyengar, S.S.,
Distributed Decision Fusion under Unknown Distributions,
OptEng(35), No. 3, March 1996, pp. 617-624. BibRef 9603

Dasarathy, B.V.,
Decision Fusion Strategies in Multi-sensor Environments,
SMC(21), No. 5, September/October 1991, pp. 1140-1154. BibRef 9109
And:
Paradigms for Information Processing in Multisensor Environments,
SPIE(1306), Sensor Fusion III, April 1990, pp. 69-80. BibRef

Dasarathy, B.V.,
Recursive Strategies for Decision Fusion in Imperfect Multisensor Environments: I Fusion Benefits,
SPIE(2233), Sensor Fusion and Aerospace Applications II, June 1994, pp. 21-32. BibRef 9406
And:
Recursive Strategies for Decision Fusion in Imperfect Multisensor Environments: II Relative Assessments,
SPIE(2233), pp. 33-44 BibRef

Dasarathy, B.V.,
Operationally Efficient Architectures for Fusion of Binary Decision Sensors in Multidecision Environments,
OptEng(36), No. 3, March 1997, pp. 632-641. 9704
BibRef

Al-Ghoneim, K.[Khaled], Kumar, B.V.K.V.[B.V.K. Vijaya],
Unified decision combination framework,
PR(31), No. 12, December 1998, pp. 2077-2089.
Elsevier DOI BibRef 9812

Rahman, A.F.R., Fairhurst, M.C., Lee, P.,
Design Considerations in the Real-Time Implementation of Multiple Expert Image Classifiers within a Modular and Flexible Multiple-platform Design Environment,
RealTimeImg(4), No. 5, October 1998, pp. 361-376.
See also New Hybrid Approach in Combining Multiple Experts to Recognize Handwritten Numerals, A.
See also Generalized-Approach to the Recognition of Structurally Similar Handwritten Characters Using Multiple Expert Classifiers. BibRef 9810

Rahman, A.F.R., Alam, H., Fairhurst, M.C.,
Multiple Classifier Combination for Character Recognition: Revisiting the Majority Voting System and Its Variations,
DAS02(167 ff.).
Springer DOI 0303
BibRef

Rahman, A.F.R., Fairhurst, M.C., Hoque, S.,
Novel approaches to optimized self-configuration in high performance multiple-expert classifiers,
FHR02(189-194).
IEEE Top Reference. 0209
BibRef

Rahman, A.F.R., Fairhurst, M.C.,
Multiple Expert Classification: A New Methodology for Parallel Decision Fusion,
IJDAR(3), No. 1, 2000, pp. 40-55. 0008
BibRef

Rahman, A.F.R.[Ahmad F.R.], Fairhurst, M.C.[Michael C.],
Decision Combination of Multiple Classifiers for Pattern Classification: Hybridisation of Majority Voting and Divide and Conquer Techniques,
WACV00(58-63).
IEEE DOI 0010
Trying to get the last percent out of classifiers. Select the ones the can be confused for specific classifiers, the ones that work well with standard techniques are done quickly. BibRef

Rahman, A.F.R., Fairhurst, M.C.,
Comparison of Some Multiple Expert Strategies: An Investigation of Resource Prerequisites and Achievable Performance,
ICPR00(Vol IV: 841-844).
IEEE DOI 0009
BibRef

Rahman, A.F.R., Fairhurst, M.C.,
Enhancing multiple expert decision combination strategies through exploitation of a priori information sources,
VISP(146), No. 1, February 1999, pp. 40. BibRef 9902

Fairhurst, M.C., Rahman, A.F.R.,
Enhancing consensus in multiple expert decision fusion,
VISP(147), No. 1, February 2000, pp. 39. 0005
BibRef

Rahman, A.F.R., Fairhurst, M.C.,
A Novel Confidence-based Framework for Multiple Expert Decision Fusion,
BMVC98(xx-yy). BibRef 9800

Rahman, A.F.R., Fairhurst, M.C.,
Multiple classifier decision combination strategies for character recognition: A review,
IJDAR(5), No. 4, July 2003, pp. 166-194.
Springer DOI 0308
BibRef

Jeon, B., Landgrebe, D.A.,
Decision Fusion Approach for Multitemporal Classification,
GeoRS(37), No. 3, May 1999, pp. 1227.
IEEE Top Reference. BibRef 9905

Gunatilaka, A.H.[Ajith H.], Baertlein, B.A.[Brian A.],
Feature-Level and Decision-Level Fusion of Noncoincidently Sampled Sensors for Land Mine Detection,
PAMI(23), No. 6, June 2001, pp. 577-589.
IEEE DOI 0106
Compare fusion at feature level and fusion at decision levle. Fusion of binary decisions (but not the case when detection confidence levels are available) does not perform better than the best sensor. Feature level fusion is better than the individual sensors. BibRef

Dasigi, V.[Venu], Mann, R.C.[Reinhold C.], Protopopescu, V.A.[Vladimir A.],
Information fusion for text classification an experimental comparison,
PR(34), No. 12, December 2001, pp. 2413-2425.
Elsevier DOI 0110
BibRef
Earlier:
Multi-sensor text classification experiments: A comparison,
TROak Ridge National Laboratory Technical Memorandum ORNL/TM-13354, Oak Ridge, TN 37831, January, 1997. Neural Nets. BibRef

Nishii, R.,
A markov random field-based approach to decision-level fusion for remote sensing image classification,
GeoRS(41), No. 10, October 2003, pp. 2316-2319.
IEEE Abstract. 0310
BibRef

Su, Y., Huang, P.S., Lin, C.F., Tu, T.M.,
Target-cluster fusion approach for classifying high resolution IKONOS imagery,
VISP(151), No. 4, August 2004, pp. 241-249.
IEEE Abstract. 0411
Within-class variability is higher for higher resolutions. BibRef

Chen, H., Meer, P.,
Robust Fusion of Uncertain Information,
SMC-B(35), No. 3, June 2005, pp. 578-586.
IEEE DOI 0508
BibRef

Narasimhamurthy, A.[Anand],
Theoretical Bounds of Majority Voting Performance for a Binary Classification Problem,
PAMI(27), No. 12, December 2005, pp. 1988-1995.
IEEE DOI 0512
BibRef
Earlier:
A Framework for the Analysis of Majority Voting,
SCIA03(268-274).
Springer DOI 0310
Formulate as optimization problem with linear constraints without assuming independence of classifiers. BibRef

Ciuonzo, D., Rossi, P.S.[P. Salvo],
Decision Fusion With Unknown Sensor Detection Probability,
SPLetters(21), No. 2, February 2014, pp. 208-212.
IEEE DOI 1402
probability BibRef

Ciuonzo, D., de Maio, A., Rossi, P.S.,
A Systematic Framework for Composite Hypothesis Testing of Independent Bernoulli Trials,
SPLetters(22), No. 9, September 2015, pp. 1249-1253.
IEEE DOI 1503
probability BibRef

Faria, F.A.[Fabio A.], dos Santos, J.A.[Jefersson A.], Rocha, A.[Anderson], da Silva Torres, R.[Ricardo],
A framework for selection and fusion of pattern classifiers in multimedia recognition,
PRL(39), No. 1, 2014, pp. 52-64.
Elsevier DOI 1402
Meta-learning BibRef

Scheirer, W.J.[Walter J.], Wilber, M.J.[Michael J.], Eckmann, M.[Michael], Boult, T.E.[Terrance E.],
Good recognition is non-metric,
PR(47), No. 8, 2014, pp. 2721-2731.
Elsevier DOI 1405
Machine learning BibRef

Wilber, M.J.[Michael J.], Rudd, E.M.[Ethan M.], Heflin, B.[Brian], Lui, Y.M.[Yui-Man], Boult, T.E.[Terrance E.],
Exemplar codes for facial attributes and tattoo recognition,
WACV14(205-212)
IEEE DOI 1406
Accuracy BibRef

Scheirer, W.J.[Walter J.], Kumar, N.[Neeraj], Belhumeur, P.N.[Peter N.], Boult, T.E.[Terrance E.],
Multi-attribute spaces: Calibration for attribute fusion and similarity search,
CVPR12(2933-2940).
IEEE DOI 1208
fusing multiple attribute scores. BibRef

Wimalajeewa, T.[Thakshila], Varshney, P.K.[Pramod K.],
Asymptotic Performance of Categorical Decision Making with Random Thresholds,
SPLetters(21), No. 8, August 2014, pp. 994-997.
IEEE DOI 1406
Collaboration BibRef

Ozdemir, O.[Onur], Allen, T.G.[Thomas G.], Choi, S.[Sora], Wimalajeewa, T.[Thakshila], Varshney, P.K.[Pramod K.],
Copula Based Classifier Fusion Under Statistical Dependence,
PAMI(40), No. 11, November 2018, pp. 2740-2748.
IEEE DOI 1810
Probability, Training data, Data models, Sensor fusion, Probability density function, Copulas, statistical dependence BibRef

Mansano, A.F.[Alex Fernandes], Matsuoka, J.A.[Jessica Akemi], Abiuzzi, N.M.[Nikolas Mota], Afonso, L.C.S.[Luis Claudio Sugi], Papa, J.P.[Joao Paulo], Faria, F.A.[Fábio A.], Torres, R.S.[Ricardo Silva], Falcao, A.X.[Alexandre Xavier],
Swarm-based Descriptor Combination and its Application for Image Classification,
ELCVIA(13), No. 3, 2014, pp. xx-yy.
DOI Link 1505
descriptor combination problem in image classification. Combine unrelated descriptors. BibRef

Yamami, A.E., Mansouri, K., Qbadou, M., Illousamen, E.H.,
Multi-criteria decision making approach for ITIL processes performance evaluation: Application to a Moroccan SME,
ISCV17(1-6)
IEEE DOI 1710
Analytic hierarchy process, Indexes, Matrix decomposition, Performance evaluation, BibRef

Rahaman, M.F., Khan, M.Z.A.,
Low-Complexity Optimal Hard Decision Fusion Under the Neyman-Pearson Criterion,
SPLetters(25), No. 3, March 2018, pp. 353-357.
IEEE DOI 1802
Bayes methods, Cognitive radio, Complexity theory, Mathematical model, Optimization, multithreshold BibRef

Tuia, D., Volpi, M., Moser, G.,
Decision Fusion With Multiple Spatial Supports by Conditional Random Fields,
GeoRS(56), No. 6, June 2018, pp. 3277-3289.
IEEE DOI 1806
Convolutional neural networks, Labeling, Lattices, Remote sensing, Semantics, Standards, Task analysis, Classification, semantic labeling BibRef

Dong, X.Y.[Xuan-Yi], Yan, Y.[Yan], Tan, M.K.[Ming-Kui], Yang, Y.[Yi], Tsang, I.W.[Ivor W.],
Late Fusion via Subspace Search With Consistency Preservation,
IP(28), No. 1, January 2019, pp. 518-528.
IEEE DOI 1810
Optimization, Robustness, Feature extraction, Manifolds, Testing, Prediction algorithms, Matrix converters, Late fusion, classification BibRef

Khan, Z.[Zubair], Kumar, S.[Shishir], Reyes, E.B.G.[Edel B. García], Mahanti, P.[Prabhat],
Multimodal fusion for pattern recognition,
PRL(115), 2018, pp. 1-3.
Elsevier DOI 1812
BibRef

Guo, B.F.[Bao-Feng],
Entropy-Mediated Decision Fusion for Remotely Sensed Image Classification,
RS(11), No. 3, 2019, pp. xx-yy.
DOI Link 1902
BibRef

Shen, J.[Junge], Zhang, C.[Chi], Zheng, Y.[Yu], Wang, R.X.[Ru-Xin],
Decision-Level Fusion with a Pluginable Importance Factor Generator for Remote Sensing Image Scene Classification,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Shi, C.[Cheng], Dang, Y.[Yenan], Fang, L.[Li], Lv, Z.Y.[Zhi-Yong], Shen, H.F.[Hui-Fang],
Attention-Guided Multispectral and Panchromatic Image Classification,
RS(13), No. 23, 2021, pp. xx-yy.
DOI Link 2112
For learning with multi sensor data. BibRef

Constantin, M.G.[Mihai Gabriel], Stefan, L.D.[Liviu-Daniel], Ionescu, B.[Bogdan],
Deepfusion: Deep Ensembles for Domain Independent System Fusion,
MMMod21(I:240-252).
Springer DOI 2106
BibRef

Asmae, A., Hussain, B.A., Souhail, S., Moukhtar, Z.E.,
A fuzzy ontology-based support for multi-criteria decision-making in collaborative product development,
ISCV17(1-6)
IEEE DOI 1710
Collaboration, Decision making, Interoperability, Mathematical model, Ontologies, Pragmatics, Semantics, Collaborative product developpment, BibRef

Li, P., Song, B.,
Land Cover Classification of Multi-sensor Images by Decision Fusion Using Weights of Evidence Model,
ISPRS12(XXXIX-B7:213-216).
DOI Link 1209
BibRef

Wang, B.[Bo], Jiang, J.Y.[Jia-Yan], Wang, W.[Wei], Zhou, Z.H.[Zhi-Hua], Tu, Z.W.[Zhuo-Wen],
Unsupervised metric fusion by cross diffusion,
CVPR12(2997-3004).
IEEE DOI 1208
BibRef

López Gutiérrez, L.[Luis], Altamirano Robles, L.[Leopoldo],
Decision Fusion for Target Detection Using Multi-spectral Image Sequences from Moving Cameras,
IbPRIA05(II:720).
Springer DOI 0509
BibRef

Gao, Y.S.[Yong-Sheng], Maggs, M.[Michael],
Feature-Level Fusion in Personal Identification,
CVPR05(I: 468-473).
IEEE DOI 0507
BibRef

Sun, Z.H.[Zhao-Hui],
Adaptation for multiple cue integration,
CVPR03(I: 440-445).
IEEE DOI 0307
Integrate multiple graphs from various cues to a single graph. BibRef

Paletta, L.[Lucas], Paar, G.[Gerhard],
Information Selection and Probabilistic 2D: 3D Integration in Mobile Mapping,
CVS03(151 ff).
Springer DOI 0306
BibRef

Soh, J.[Jung],
Combination of Decisions by Multiple Document Object Locators,
VI02(198).
PDF File. 0208
BibRef

Hong, P.Y.[Peng-Yu], Huang, T.S.,
Multimodal temporal pattern mining,
ICPR02(III: 465-468).
IEEE DOI 0211
BibRef

Hong, P.Y.[Peng-Yu], Wang, R.[Roy], Huang, T.S.[Thomas S.],
Learning Patterns from Images by Combining Soft Decisions and Hard Decisions,
CVPR00(I: 78-83).
IEEE DOI 0005
BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
King Sun Fu Pattern Recognition Papers .