Invariant pattern descriptor-based logo recognition using radon transform and complex moments

In this paper, a novel logo recognition algorithm based on a set of invariant features, which are calculated by using Radon transform and complex moments is proposed. This set of features is invariant to Rotation, Scaling, and Translation (RST) and it is also robust to additive noise. Radon transform is powerful tool for rotation, scaling, and translation properties which make it useful for our purpose. To obtain the RST invariant features, at first Radon transform is applied to logo image and then the complex moments are calculated from the radial and angular coordinates of Radon image. Logo recognition is carried out based on a similarity-based strategy by using Normalized Cross Correlation (NCC) measure. The proposed algorithm is evaluated on the UMD logo database (including 106 classes of logos). The experimental results validate the effectiveness of our algorithm in logo recognition and its robustness to additive noise.