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Memetic Computing
Erschienen in: Memetic Computing 3/2016

01.09.2016 | Regular Research Paper

A memetic-based fuzzy support vector machine model and its application to license plate recognition

verfasst von: Hussein Samma, Chee Peng Lim, Junita Mohamad Saleh, Shahrel Azmin Suandi

Erschienen in: Memetic Computing | Ausgabe 3/2016

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Abstract

In this paper, a novel fuzzy support vector machine (FSVM) coupled with a memetic particle swarm optimization (MPSO) algorithm is introduced. Its application to a license plate recognition problem is studied comprehensively. The proposed recognition model comprises linear FSVM classifiers which are used to locate a two-character window of the license plate. A new MPSO algorithm which consists of three layers i.e. a global optimization layer, a component optimization layer, and a local optimization layer is constructed. During the construction process, MPSO performs FSVM parameters tuning, feature selection, and training instance selection simultaneously. A total of 220 real Malaysian car plate images are used for evaluation. The experimental results indicate the effectiveness of the proposed model for undertaking license plate recognition problems.
Vorheriger Artikel Rational and self-adaptive evolutionary extreme learning machine for electricity price forecast

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Literatur
1.
Anagnostopoulos CNE, Anagnostopoulos IE, Psoroulas ID, Loumos V, Kayafas E (2008) License plate recognition from still images and video sequences: a survey. Intell Transp Syst IEEE Trans 9(3):377–391CrossRef
2.
Shan D, Ibrahim M, Shehata M, Badawy W (2013) Automatic license plate recognition (ALPR): a state-of-the-art review. Circ Syst Video Technol IEEE Trans 23(2):311–325CrossRef
3.
4.
5.
Kim K, Jung K, Kim J (2002) Color texture-based object detection: an application to license plate localization. In: Lee SW, Verri A (eds) Pattern recognition with support vector machines. Springer, Berlin, Heidelberg, pp 293–309CrossRef
6.
Yao Z, Yi W (2014) License plate detection based on multistage information fusion. Inf Fusion 18:78–85CrossRef
7.
Jin-Tsong J (2005) Hybrid approach of selecting hyperparameters of support vector machine for regression. Syst Man Cybern Part B Cybern IEEE Trans 36(3):699–709CrossRef
8.
Steinwart I (2003) On the optimal parameter choice for v-support vector machines. Pattern Anal Mach Intell IEEE Trans 25(10):1274–1284CrossRef
9.
Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. In: IEEE computer society conference on computer vision and pattern recognition, vol 1, pp 511–518
10.
Guo XC, Yang JH, Wu CG, Wang CY, Liang YC (2008) A novel LS-SVMs hyper-parameter selection based on particle swarm optimization. Neurocomputing 71(16):3211–3215CrossRef
11.
Huang C-L, Dun J-F (2008) A distributed PSO-SVM hybrid system with feature selection and parameter optimization. Appl Soft Comput 8(4):1381–1391CrossRef
12.
Lin C, Ying K, Chen S, Lee Z (2008) Particle swarm optimization for parameter determination and feature selection of support vector machines. Expert Syst Appl 35(4):1817–1824CrossRef
13.
Sudheer C, Maheswaran R, Panigrahi B, Mathur S (2013) A hybrid SVM-PSO model for forecasting monthly streamflow. Neural Comput Appl 24(6):1381–1389CrossRef
14.
Wang H, Zhao G, Li N (2012) Training support vector data descriptors using converging linear particle swarm optimization. Neural Comput Appl 21(6):1099–1105CrossRef
15.
Batuwita R, Palade V (2010) FSVM-CIL: fuzzy support vector machines for class imbalance learning. Fuzzy Syst IEEE Trans 18(3):558–571CrossRef
16.
Chun-Fu L, Sheng-De W (2002) Fuzzy support vector machines. Neural Netw IEEE Trans 13(2):464–471CrossRef
17.
António C (2014) A memetic algorithm based on multiple learning procedures for global optimal design of composite structures. Memet Comput 6(2):113–131CrossRef
18.
Raeesi NM, Kobti Z (2012) A memetic algorithm for job shop scheduling using a critical-path-based local search heuristic. Memet Comput 4(3):231–245CrossRef
19.
Nekkaa M, Boughaci D (2015) A memetic algorithm with support vector machine for feature selection and classification. Memet Comput 7(1):59–73CrossRef
20.
Ni J, Li L, Qiao F, Wu Q (2013) A novel memetic algorithm and its application to data clustering. Memet Comput 5(1):65–78CrossRef
21.
Goldbarg M, Asconavieta P, Goldbarg E (2012) Memetic algorithm for the traveling car renter problem: an experimental investigation. Memet Comput 4(2):89–108CrossRef
22.
Chia J, Goh C, Tan K, Shim V (2011) Memetic informed evolutionary optimization via data mining. Memet Comput 3(2):73–87CrossRef
23.
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297MATH
24.
Sutton RS, Precup D, Singh S (1999) Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning. Artif Intell 112(12):181–211MathSciNetMATHCrossRef
25.
McPartland M, Gallagher M (2011) Reinforcement learning in first person shooter games. Comput Intell AI Games IEEE Trans 3(1):43–56CrossRef
26.
Sharma R, Spaan MTJ (2012) Bayesian-game-based fuzzy reinforcement learning control for decentralized POMDPs. Comput Intell AI Games IEEE Trans 4(4):309–328CrossRef
27.
Watkins CCH, Dayan P (1992) Q-learning. Mach Learn 8(3–4):279–292MATH
28.
Rakshit P, Konar A, Bhowmik P, Goswami I, Das S, Jain L, Nagar A (2013) Realization of an adaptive memetic algorithm using differential evolution and q-learning: a case study in multirobot path planning. Syst Man Cybern Syst IEEE Trans 43(4):814–831CrossRef
29.
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: IEEE International Conference on Neural Networks. Perth, WA
30.
Zhi-Hui Z, Jun Z, Yun L, Chung H (2009) Adaptive particle swarm optimization. Syst Man Cybern Part B Cybern IEEE Trans 39(6):1362–1381CrossRef
31.
Lim WH, Mat Isa NA (2013) Two-layer particle swarm optimization with intelligent division of labor. Eng Appl Artif Intell 26(10):2327–2348CrossRef
32.
Lim WH, Mat Isa NA (2014) Teaching and peer-learning particle swarm optimization. Appl Soft Comput 18:39–58CrossRef
33.
Lim WH, Mat Isa NA (2014) Particle swarm optimization with increasing topology connectivity. Eng Appl Artif Intell 27:80–102CrossRef
34.
Zhi-Hui Z, Jun Z, Yun L, Yu-hui S (2010) Orthogonal learning particle swarm optimization. Evol Comput IEEE Trans 15(6):832–847
35.
Gao M, Hong X, Chen S, Harris C (2011) A combined SMOTE and PSO based RBF classifier for two-class imbalanced problems. Neurocomputing 74(17):3456–3466CrossRef
36.
Xia H, Sheng C, Harris CJ (2007) A kernel-based two-class classifier for imbalanced data sets. Neural Netw IEEE Trans 18(1):28–41CrossRef
37.
Caner H, Gecim HS, Alkar AZ (2008) Efficient embedded neural-network-based license plate recognition system. Veh Technol IEEE Trans 57(5):2675–2683CrossRef
38.
Bo L, Bin T, Ye L, Ding W (2013) Component-based license plate detection using conditional random field model. Intell Transp Syst IEEE Trans 14(4):1690–1699CrossRef
39.
Zhou W, Li H, Yijuan L, Qi T (2012) Principal visual word discovery for automatic license plate detection. Image Process IEEE Trans 21(9):4269–4279MathSciNetCrossRef
40.
Agarwal S, Awan A, Roth D (2004) Learning to detect objects in images via a sparse, part-based representation. Pattern Anal Mach Intell IEEE Trans 26(11):1475–1490CrossRef
41.
Zhen J, Huilian L, Yiwei W, Wu Q (2007) A novel intelligent particle optimizer for global optimization of multimodal functions. In: IEEE congress on evolutionary computation. Singapore
42.
Pan Q-K, Fatih Tasgetiren M, Liang Y-C (2008) A discrete particle swarm optimization algorithm for the no-wait flowshop scheduling problem. Comput Oper Res 35(9):2807–2839MathSciNetMATHCrossRef
43.
Wang C-H, Lin T-W (2011) Improved particle swarm optimization to minimize periodic preventive maintenance cost for series-parallel systems. Expert Syst Appl 38(7):8963–8969CrossRef
44.
Bishop CM (1995) Neural networks for pattern recognition. Oxford University Press, Oxford IncMATH
45.
Friedman N, Geiger D, Goldszmidt M (1997) Bayesian network classifiers. Mach Learn 29(2–3):131–163MATHCrossRef
46.
Aha D, Kibler D, Albert M (1991) Instance-based learning algorithms. Mach Learn 6(1):37–66
47.
Han H-G, Qiao J-F (2013) A structure optimisation algorithm for feedforward neural network construction. Neurocomputing 99:347–357CrossRef
48.
Efrorn B, Tibshirani RJ (1993) An introduction to the bootstrap. Chapman and Hall, LondonCrossRef
49.
50.
Pham D, Ghanbarzadeh A, Koc E, Otri S, Rahim S, Zaidi M (2005) The bees algorithm. Technical note. Manufacturing Engineering Centre, Cardiff University, UK, pp 1–57
51.
Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68CrossRef
52.
Mirjalili S, Mirjalili S, Hatamlou A (2015) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27(3):495–513
53.
Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61CrossRef
54.
Hiromoto M, Sugano H, Miyamoto R (2009) Partially parallel architecture for adaboost-based detection with haar-like features. Circ Syst Video Technol IEEE Trans 19(1):41–52CrossRef
55.
Wang Q, Yan P, Yuan Y, Li X (2013) Multi-spectral saliency detection. Pattern Recognit Lett 34(1):34–41CrossRef
56.
Naqvi S, Browne WN, Hollitt C (2016) Salient object detection via spectral matting. Pattern Recognit 51:209–224CrossRef
57.
Chen S, Liu C (2015) Eye detection using discriminatory Haar features and a new efficient SVM. Image Vis Comput 33:68–77CrossRef
58.
Park K-Y, Hwang S-Y (2014) An improved Haar-like feature for efficient object detection. Pattern Recognit Lett 42:148–153CrossRef
59.
Vural S, Mae Y, Uvet H, Arai T (2012) Multi-view fast object detection by using extended haar filters in uncontrolled environments. Pattern Recognit Lett 33(2):126–133CrossRef
60.
Yan J, Li J, Gao X (2011) Chinese text location under complex background using Gabor filter and SVM. Neurocomputing 74(17):2998–3008CrossRef
61.
Paisitkriangkrai S, Chunhua S, Zhang J (2008) Fast pedestrian detection using a cascade of boosted covariance features. Circ Syst Video Technol IEEE Trans 18(8):1140–1151CrossRef
Metadaten
Titel
A memetic-based fuzzy support vector machine model and its application to license plate recognition
verfasst von
Hussein Samma
Chee Peng Lim
Junita Mohamad Saleh
Shahrel Azmin Suandi
Publikationsdatum
01.09.2016
Verlag
Springer Berlin Heidelberg
Erschienen in
Memetic Computing / Ausgabe 3/2016
Print ISSN: 1865-9284
Elektronische ISSN: 1865-9292
DOI
https://doi.org/10.1007/s12293-016-0187-0

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