Abstract
In this paper, we introduce a modular framework to detect noisy logo appearing on online merchandise images so as to support the forensics investigation and detection of increasing online counterfeit product trading and fraud cases. The proposed framework and system is able to perform an automatic logo image classification on realistic and noisy product images. The novel contributions in this work include the design of a modular SVM-based logo classification framework, and its internal segmentation module, two new feature extractions modules, and the decision algorithm for noisy logo detection. We developed the system to perform an automated multi-class product images classification, which achieves promising results on logo classification experiments of Louis Vuitton, Chanel and Polo Ralph Lauren.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
International Authentication Association, “Counterfeit statistics.” (2010), http://internationalauthenticationassociation.org/content/counterfeit_statistics.php
Otim, S., Grover, V.: E-commerce: a brand name’s curse. Electronic Markets 20(2), 147–160 (2010)
Zhu, G., Doermann, D.: Automatic document logo detection. In: Proc. 9th Int. Conf. Document Analysis and Recognition (ICDAR 2007), pp. 864–868 (2007)
Zhu, G., Doermann, D.: Logo matching for document image retrieval. In: Proceedings of the 2009 10th International Conference on Document Analysis and Recognition, pp. 606–610 (2009)
Wang, H., Chen, Y.: Logo detection in document images based on boundary extension of feature rectangles. In: Proceedings of the 2009 10th International Conference on Document Analysis and Recognition, pp. 1335–1339 (2009)
Rusinol, M., Llados, J.: Logo spotting by a bag-of-words approach for document categorization. In: Proceedings of the 2009 10th International Conference on Document Analysis and Recognition, pp. 111–115 (2009)
Li, Z., Schulte-Austum, M., Neschen, M.: Fast logo detection and recognition in document images. In: Proceedings of the 2010 20th International Conference on Pattern Recognition, pp. 2716–2719 (2010)
Sun, S.-K., Chen, Z.: Robust logo recognition for mobile phone applications. J. Inf. Sci. Eng. 27(2), 545–559 (2011)
Viola, P., Jones, M.: 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 (2001)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 886–893 (2005)
Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning 20, 273–297 (1995)
Joachims, T.: Making large-scale svm learning practical. In: Schlkopf, B., Burges, C., Smola, A. (eds.) Making large-Scale SVM Learning Practical. Advances in Kernel Methods - Support Vector Learning. MIT-Press (1999)
Chang, C.-C., Lin, C.-J.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 27:1–27:27 (2011), http://www.csie.ntu.edu.tw/~cjlin/libsvm
Lowe, D.G.: Object recognition from local scale-invariant features. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, pp. 1150–1157 (1999)
Deng, H., Zhang, W., Mortensen, E., Dietterich, T.: Principal curvature-based region detector for object recognition. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)
Suzuki, S., Abe, K.: Topological structural analysis of digitized binary images by border following. Computer Vision, Graphics, and Image Processing 30, 32–46 (1985)
Canny, J.F.: A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 8, 679–698 (1986)
Avrithis, Y.S., Xirouhakis, Y., Kollias, S.D.: Affine-invariant curve normalization for shape-based retrieval. In: 15th International Conference on Pattern Recognition, vol. 1, pp. 1015–1018 (2000)
Zhang, D., Lu, G.: A comparative study on shape retrieval using fourier descriptors with different shape signatures. Journal of Visual Communication and Image Representation 14, 41–60 (2003)
Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3, 71–86 (1991)
Tuzel, O., Porikli, F., Meer, P.: Region Covariance: A fast Descriptor for Detection and Classification. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part II. LNCS, vol. 3952, pp. 589–600. Springer, Heidelberg (2006)
Rodgers, J.L., Nicewander, A.W.: Thirteen ways to look at the correlation coefficient. The American Statistician 42, 59–66 (1988)
Hsu, C.-W., Lin, C.-J.: A comparison of methods for multiclass support vector machines. IEEE Transactions on Neural Networks 13, 415–425 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Thing, V.L.L., Lim, WY., Zeng, J., Tan, D.J.J., Chen, Y. (2011). Design of a Modular Framework for Noisy Logo Classification in Fraud Detection. In: Kim, Th., Adeli, H., Fang, Wc., Villalba, J.G., Arnett, K.P., Khan, M.K. (eds) Security Technology. SecTech 2011. Communications in Computer and Information Science, vol 259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27189-2_6
Download citation
DOI: https://doi.org/10.1007/978-3-642-27189-2_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27188-5
Online ISBN: 978-3-642-27189-2
eBook Packages: Computer ScienceComputer Science (R0)