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A new automatic hybrid classifier for natural images by combining two base classifiers through the fuzzy cognitive maps (FCMs) approach is presented in this study. The base classifiers used are fuzzy clustering (FC) and the parametric Bayesian (BP) method. During the training phase, different partitions are established until a valid partition is found. Partitioning and validation are two automatic processes based on validation measurements. From a valid partition, the parameters of both classifiers are estimated. During the classification phase, FC provides for each pixel the supports (membership degrees) that determine which cluster the pixel belongs to. These supports are punished or rewarded based on the supports (probabilities) provided by BP. This is achieved through the FCM approach, which combines the different supports. The automatic strategy and the combined strategy under the FCM framework make up the main findings of this study. The analysis of the results shows that the performance of the proposed method is superior to other hybrid methods and more accurate than the single usage of existing base classifiers.
References
-
-
1)
-
R.R. Yager
.
On ordered weighted averaging aggregation operators in multicriteria decision making.
IEEE Trans. Syst. Man Cybern.
,
1 ,
183 -
190
-
2)
-
Z. Volkovich ,
Z. Barzily ,
L. Morozensky
.
A statistical model of cluster stability.
Pattern Recognit.
,
7 ,
2174 -
2188
-
3)
-
J. Bezdek
.
(1981)
Pattern recognition with fuzzy objective function algorithms.
-
4)
-
P. Maillard
.
Comparing texture analysis methods through classification.
Photogram. Eng. Remote Sens.
,
4 ,
357 -
367
-
5)
-
Kong, Z., Cai, Z.: `Advances of research in fuzzy integral for classifier's fusion', Proc. Eighth ACIS Int. Conf. Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, 2007, 2, p. 809–814.
-
6)
-
R.O. Duda ,
P.E. Hart ,
D.G. Stork
.
Pattern classification.
-
7)
-
A. Frank ,
A. Asuncion
.
UCI machine learning repository.
-
8)
-
B. Balasko ,
J. Abonyi ,
B. Feil
.
(2008)
Fuzzy clústering and data analysis toolbox for use with Matlab.
-
9)
-
R.M. Valdovinos ,
J.S. Sánchez ,
R. Barandela ,
J.S. Marques ,
N. Pérez de la Blanca ,
P. Pina
.
(2005)
Dynamic and static weighting in classifier fusion, Pattern recognition and image analysis.
-
10)
-
J. Aguilar
.
A survey about fuzzy cognitive maps papers.
Int. J. Comput. Cogn.
,
2 ,
27 -
33
-
11)
-
J. Kittler
.
On combining classifiers.
IEEE Trans. Pattern Anal. Mach. Intell.
,
3 ,
226 -
239
-
12)
-
S. Kumar ,
J. Ghosh ,
M.M. Crawford
.
Best-bases feature extraction for pairwise classification of hyperspectral data.
IEEE Trans. Geosci. Remote Sens.
,
7 ,
1368 -
1379
-
13)
-
B. Kosko
.
Fuzzy cognitive maps.
Int. J. Man Mach. Stud.
,
65 -
75
-
14)
-
L.I. Kuncheva
.
(2004)
Combining pattern classifiers: methods and algorithms.
-
15)
-
Martchenko, A.S., Ermolov, I.L., Groumpos, P.P., Poduraev, J.V., Stylios, C.D.: `Investigating stability analysis issues for fuzzy cognitive maps', 11thMediterranean Conf. Control and Automation, 2003, p. 619–624.
-
16)
-
A.K. Tsardias ,
K.G. Margaritis
.
An experimental study of the dynamics of the certainty neuron fuzzy cognitive maps.
Neurocomputing
,
95 -
116
-
17)
-
B. Kosko
.
(1992)
Neural networks and fuzzysystems: a dynamical systemsapproach to machine intelligence.
-
18)
-
Y. Miao ,
Z.Q. Liu
.
On causal inference in fuzzy cognitive maps.
IEEE Trans. Fuzzy Syst.
,
1 ,
107 -
119
-
19)
-
A.K. Tsardias ,
K.G. Margaritis
.
Cognitive mapping and certainty neuron fuzzy cognitive maps.
Inf. Sci.
,
109 -
130
-
20)
-
D. Partridge ,
N. Griffith
.
Multiple classifier systems: software engineered, automatically modular leading to a taxonomic overview.
Pattern Anal. Appl.
,
180 -
188
-
21)
-
D. Deng ,
J. Zhang
.
Combining multiple precision-boosted classifiers for indoor-outdoor scene classification.
Inf. Technol. Appl.
,
720 -
725
-
22)
-
H. Zimmermann
.
(2001)
Fuzzy set theory and its applications.
-
23)
-
L.I. Kuncheva
.
“Fuzzy” vs “non-fuzzy” in combining classifiers designed by boosting.
IEEE Trans. Fuzzy Syst.
,
6 ,
729 -
741
-
24)
-
Yu, H., Li, M., Zhang, H.J., Feng, J.: `Color texture moments for content-based image retrieval', Proc. Int. Conf. Image Processing, 2002, 3, p. 24–28.
-
25)
-
J.B.D. Cabrera
.
On the impact of fusion strategies on classification errors for large ensambles of classifiers.
Pattern Recognit.
,
1963 -
1978
-
26)
-
B.G. Buchanan ,
E.H. Shorliffe
.
(1984)
Rule-based expert systems. The MYCIN experiments of the Stanford Heuristic Programming Project.
-
27)
-
Cao, J., Shridhar, M., Ahmadi, M.: `Fusion of classifiers with fuzzy integrals', Proc. Third Int. Conf. Document Analysis and Recognition (ICDAR'95), 1995, 1, p. 108–111.
-
28)
-
S. Kumar ,
J. Ghosh ,
M.M. Crawford
.
Hierarchical fusion of multiple classifiers for hyperspectral data analysis.
Pattern Anal. Appl.
,
210 -
220
-
29)
-
L.A. Alexandre ,
A.C. Campilho ,
M. Kamel
.
On combining classifiers using sum and product rules.
Pattern Recognit. Lett.
,
1283 -
1289
-
30)
-
T. Randen ,
J.H. Husoy
.
Filtering for texture classification: a comparative study.
Trans. Pattern Anal. Mach. Intell.
,
4 ,
291 -
310
-
31)
-
Q. Cheng ,
Z.T. Fang
.
The stability problem for fuzzy bidirectional associative memories.
Fuzzy Sets Syst.
,
83 -
90
-
32)
-
A. Dr.imbarean ,
P.F. Whelan
.
Experiments in colour texture analysis.
Pattern Recognit. Lett.
,
4 ,
1161 -
1167
-
33)
-
R. Rud ,
M. Shoshany ,
V. Alchanatis ,
Y. Cohen
.
Application of spectral features' ratios for improving classification in partially calibrated hyperspectral imagery: a case study of separating Mediterranean vegetation species.
J. Real-Time Image Process.
,
143 -
152
-
34)
-
Hanmandlu, M., Madasu, V.K., Vasikarla, S.: `A fuzzy approach to texture segmentation', Proc. IEEE Int. Conf. Information Technology: Coding and Computing (ITCC'04), 2004, The Orleans, Las Vegas, Nevada, USA, p. 636–642.
-
35)
-
E.H. Shorliffe
.
(1976)
Computer-based medical consultations: MYCIN.
-
36)
-
A.K. Tsardias ,
K.G. Margaritis
.
The MYCIN certainty factor handling as uniform operator and its use as threshold function in artificial neurons.
Fuzzy Sets Syst.
,
263 -
274
-
37)
-
Stach, W., Kurgan, L., Pedrycz, W., Reformat, M.: `Evolutionary development of fuzzy cognitive maps', Proc. IEEE Conf. Fuzzy Syst., 2005, p. 619–624.
-
38)
-
D. Puig ,
M.A. García
.
Automatic texture feature selection for image pixel classification.
Pattern Recognit.
,
11 ,
1996 -
2009
-
39)
-
G. Smith ,
I. Burns
.
Measuring texture classification algorithms.
Pattern Recognit. Lett.
,
1495 -
1501
-
40)
-
T. Wagner ,
B. Jähne ,
H. Hauβecker ,
P. Geiβler
.
(1999)
Texture analysis, Handbook of computer vision and applications.
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