14.1.5.3 Decision Fusion

Chapter Contents (Back)
Decision Fusion. Knowledge-Based Vision. Combination. 0305
See also Information Fusion, Sensor 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 Abstract. IEEE Top Reference.
WWW Version. 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 Abstract. IEEE Top Reference.
WWW Version. BibRef 9401
Earlier:
On multiple classifier systems for pattern recognition,
ICPR92(II:84-87).
IEEE DOI Link 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.
WWW Version. 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.).
HTML Version. 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 Abstract. IEEE Top Reference. 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 Link
HTML Version. 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.
HTML Version. 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 Abstract. IEEE Top Reference.
WWW Version. 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.
WWW Version. 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. 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. IEEE Top Reference. 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. IEEE Top Reference. 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 Link 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 Link 0512
BibRef
Earlier:
A Framework for the Analysis of Majority Voting,
SCIA03(268-274).
WWW Version. 0310
Formulate as optimization problem with linear constraints without assuming independence of classifiers. 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 Link 0509
BibRef

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

Sun, Z.H.[Zhao-Hui],
Adaptation for multiple cue integration,
CVPR03(I: 440-445).
IEEE Abstract. IEEE Top Reference. 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).
HTML Version. 0306
BibRef

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

Hong, P.Y.[Peng-Yu], Huang, T.S.,
Multimodal temporal pattern mining,
ICPR02(III: 465-468).
IEEE DOI Link 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 Abstract. IEEE Top Reference.
WWW Version. 0005
BibRef

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


Last update:Nov 16, 2009 at 19:35:14