A Noise-Tolerant Enhanced Classification Method for Logo Detection and Brand Classification

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.

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