Abstract
The performance of different feature extraction and shape description methods in trademark image recognition systems have been studied by several researchers. However, the potential improvement in classification through feature fusion by ensemble-based methods has remained unattended. In this work, we evaluate the performance of an ensemble of three classifiers, each trained on different feature sets. Three promising shape description techniques, including Zernike moments, generic Fourier descriptors, and shape signature are used to extract informative features from logo images, and each set of features is fed into an individual classifier. In order to reduce recognition error, a powerful combination strategy based on the Dempster-Shafer theory is utilized to fuse the three classifiers trained on different sources of information. This combination strategy can effectively make use of diversity of base learners generated with different set of features. The recognition results of the individual classifiers are compared with those obtained from fusing the classifiers’ output, showing significant performance improvements of the proposed methodology.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Alajlan, N., Kamel, M., Freeman, G.: Multi-object image retrieval based on shape and topology. Signal Processing: Image Communication 21(10), 904–918 (2006)
Brodley, A., Lane, T.: Creating and exploiting coverage and diversity. In: Proceedings of AAAI 1996 Workshop on Integrating Multiple Learned Models, Portland, OR, pp. 8–14 (1996)
Chang, C.C., Lin, C.J.: Libsvm: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2(3), 1–27 (2011)
Ciocca, G., Schettini, R.: Content-based similarity retrieval of trademarks using relevance feedback. Pattern Recognition 34(8), 1639–1655 (2001)
Cortelazzo, G., Mian, G., Vezzi, G., Zamperoni, P.: Trademark shapes description by string-matching techniques. Pattern Recognition 27(8), 1005–1018 (1994)
Dempster, A.: Upper and lower probabilities induced by multivalued mappings. Annals of Mathematical Statistics 38(2), 325–339 (1967)
Dietterich, T.G.: Machine learning research: Four current directions. Artificial Intell. Mag. 18(4), 97–136 (1997)
Doermann, D., Rivlin, E., Weiss, I.: Applying algebraic and differential invariants for logo recognition. Machine Vision and Applications 9(2), 73–86 (1996)
Escalera, S., Fornés, A., Pujol, O., Lladós, J., Radeva, P.: Circular blurred shape model for multiclass symbol recognition. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 41(2), 497–506 (2011)
Freund, Y., Schapire, R.: A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55(1), 119–139 (1997)
García-Pedrajas, N., Ortiz-Boyer, D.: An empirical study of binary classifier fusion methods for multiclass classification. Information Fusion 12(2), 111–130 (2011)
Ho, T.K.: The random subspace method for constructing decision forests. IEEE Transactions on Pattem Analysis and Machine Intelligence 20, 832–844 (1998)
Hwang, S., Kim, W.: A novel approach to the fast computation of zernike moments. Pattern Recognition 39(11), 2065–2076 (2006)
Jain, A., Vailaya, A.: Shape-based retrieval: A case study with trademark image databases. Pattern Recognition 31(9), 1369–1390 (1998)
Kim, Y., Kim, W.: Content-based trademark retrieval system using a visually salient feature. Image and Vision Computing 16(12-13), 931–939 (1998)
Kittler, J., Hatef, M., Duin, R., Matas, J.: On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Inteligence 20(3), 226–239 (1998)
Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. Wiley, New York (2004)
Li, S., Lee, M., Pun, C.: Complex zernike moments features for shape-based image retrieval. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 39(1), 227–237 (2009)
Mohd Anuar, F., Setchi, R., Lai, Y.K.: Trademark image retrieval using an integrated shape descriptor. Expert Systems with Applications (2012), http://dx.doi.org/10.1016/j.eswa.2012.07.031
Neumann, J., Samet, H., Soffer, A.: Integration of local and global shape analysis for logo classification. Pattern Recognition Letters 23, 1449–1457 (2002)
Opitz, D.W.: Feature selection for ensembles (1999)
Polikar, R.: Ensemble based systems in decision making. IEEE Circuits and Systems Magazine 6(3), 21–45 (2006)
Qi, H., Li, K., Shen, Y., Qu, W.: An effective solution for trademark image retrieval by combining shape description and feature matching. Pattern Recognition 43(6), 2017–2027 (2010)
Rogova, G.: Combining the results of several neural network classifiers. Neural Networks 7, 777–781 (1994)
Seiden, S., Dillencourt, M., Irani, S., Borrey, R., Murphy, T.: Logo detection in document images. In: International Conference on Imaging Science, Systems, and Technology, Las Vegas, Nevada, pp. 446–449 (1997)
Zhang, D., Lu, G.: Shape-based image retrieval using generic fourier descriptor. Signal Processing: Image Communication 17(10), 825–848 (2002)
Zhang, D., Lu, G.: Study and evaluation of different fourier methods for image retrieval. Image and Vision Computing 23(1), 33–49 (2005)
Zhang, D., Lu, G.: Review of shape representation and description techniques. Pattern Recognition 37(1), 1–19 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bagheri, M.A., Gao, Q., Escalera, S. (2013). Logo Recognition Based on the Dempster-Shafer Fusion of Multiple Classifiers. In: Zaïane, O.R., Zilles, S. (eds) Advances in Artificial Intelligence. Canadian AI 2013. Lecture Notes in Computer Science(), vol 7884. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38457-8_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-38457-8_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38456-1
Online ISBN: 978-3-642-38457-8
eBook Packages: Computer ScienceComputer Science (R0)