Logo Recognition System Using Angular Radial Transform Descriptors

Abstract

Problem statement: The shape-based logo recognition systems have been developed to automate the logo registration process. The logo recognition operation faces many challenges such as having to recognize logos that might be scaled, rotated, translated and added with noises. Different types of logo’s shapes further add to the complex nature of this problem. Approach: We developed a logo recognition system that comprises of three phases: Preprocessing, feature extraction and features matching. For feature extraction, we adopted a region-based Angular Radial Transform (ART) to extract the features from logo’s shapes. We used the Euclidian Distance (ED) as a similarity measure parameter for the features matching. Results: We tested the system that used the ART as feature extraction method on a large logo database of 2730 images to investigate the effect of several deformations and noise on recognition performance. The experimental results showed the system that use the ART features was robust against the size changing, had an excellent discrimination power against different types of noises and good immunity to rotations. The performance evaluation results showed that ART technique perform better than Zernike moments and Invariant moment’s techniques. Conclusion: The proposed ART descriptor was very effective to describe all types of logo’s shapes independent on different types of deformations and noise. It also represented the logo’s shapes in concise manner without information redundancy.