Design of a Modular Framework for Noisy Logo Classification in Fraud Detection

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.

[1]  Yannis Avrithis,et al.  Affine-invariant curve normalization for shape-based retrieval , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[2]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[3]  Thorsten Joachims,et al.  Making large scale SVM learning practical , 1998 .

[4]  Zhe Li,et al.  Fast Logo Detection and Recognition in Document Images , 2010, 2010 20th International Conference on Pattern Recognition.

[5]  Youbin Chen,et al.  Logo Detection in Document Images Based on Boundary Extension of Feature Rectangles , 2009, 2009 10th International Conference on Document Analysis and Recognition.

[6]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[7]  Varun Grover,et al.  E-commerce: a brand name’s curse , 2010, Electron. Mark..

[8]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[10]  Antti Oulasvirta,et al.  Computer Vision – ECCV 2006 , 2006, Lecture Notes in Computer Science.

[11]  David S. Doermann,et al.  Logo Matching for Document Image Retrieval , 2009, 2009 10th International Conference on Document Analysis and Recognition.

[12]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[13]  David Doermann,et al.  Automatic Document Logo Detection , 2007 .

[14]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[15]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[16]  Fatih Murat Porikli,et al.  Region Covariance: A Fast Descriptor for Detection and Classification , 2006, ECCV.

[18]  Keiichi Abe,et al.  Topological structural analysis of digitized binary images by border following , 1985, Comput. Vis. Graph. Image Process..

[19]  Zen Chen,et al.  Robust Logo Recognition for Mobile Phone Applications , 2011, J. Inf. Sci. Eng..

[20]  Chih-Jen Lin,et al.  A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

[21]  Thomas G. Dietterich,et al.  Principal Curvature-Based Region Detector for Object Recognition , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[22]  Miguel Figueroa,et al.  Competitive learning with floating-gate circuits , 2002, IEEE Trans. Neural Networks.

[23]  J. Rodgers,et al.  Thirteen ways to look at the correlation coefficient , 1988 .

[24]  Josep Lladós,et al.  Logo Spotting by a Bag-of-words Approach for Document Categorization , 2009, 2009 10th International Conference on Document Analysis and Recognition.