Abstract
This paper introduces a Noise-Tolerant Enhanced Classification (N-TEC) approach to monitor and prevent the increasing online counterfeit product trading attempts and frauds. The proposed approach is able to perform an automatic logo image classification at a fast speed on realistic and noisy product pictures. The novel contribution is three-fold: (i) design of a self adjustable cascade classifier training approach to achieve strong noise tolerance in training, (ii) design of a Stage Selection Optimization (SSO) method which is compatible with the training approach to improve the classification speed and the detection accuracy, (iii) development of an automatic classification system which achieves promising logo detection and brand classification results.
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.
Similar content being viewed by others
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)
Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55(1), 119–139 (1997)
Friedman, J., Hastie, T., et al.: Additive logistic regression: A statistical view of boosting. Annals of Statistics 28(2), 337–374 (2000)
Zhang, C., Zhang, Z.: Boosting-Based Face Detection and Adaptation. Synthesis Lectures on Computer Vision. Morgan and Claypool (2010)
Zhang, C., Zhang, Z.: A Survey of Recent Advances in Face Detection. Microsoft Research Technical Report, MSR-TR-2010-66 (2010)
Castrillon, M., Deniz, O., et al.: A comparison of face and facial feature detectors based on the Viola-Jones general object detection framework. Machine Vision and Applications 22(3), 481–494 (2011)
Li, S.Z., Zhang, Z.Q.: FloatBoost learning and statistical face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(9), 1112–1123 (2004)
Viola, P., Jones, M.J., et al.: Detecting pedestrians using patterns of motion and appearance. International Journal of Computer Vision 63(2), 153–161 (2005)
Mita, T., Kaneko, T., Hori, O.: Joint haar-like features for face detection. In: 10th In ICCV (2005)
Viola, P., Platt, J., Zhang, C.: Multiple instance boosting for object detection. In: Advances in Neural Information Processing Systems (2005)
Viola, P., Jones, M.J.: Robust real-time face detection. International Journal of Computer Vision 57(2), 137–154 (2004)
Lienhart, R., Maydt, J.: An extended set of haar-like features for rapid object detection. In: Proceedings of 2002 International Conference on Image Processing, vol. I, pp. 900–903 (2002)
Tuzel, O., Porikli, F., et al.: Pedestrian detection via classification on Riemannian manifolds. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(10), 1713–1727 (2008)
EBAY: Images of search results, http://www.ebay.com/
Rowley, H.A., Baluja, S., et al.: Neural network-based face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(1), 23–38 (1998)
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
Chen, Y., Thing, V.L.L. (2011). A Noise-Tolerant Enhanced Classification Method for Logo Detection and Brand Classification. 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_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-27189-2_4
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)