Abstract
In this paper, we propose a Vehicle Logo Recognition (VLR) approach which uses moment invariant for feature extraction. Moment invariants and Minimum-Mean Distance (MMD) classifier are adopted to recognize six different types of vehicle logos from a public dataset. Vehicle logos obtained from coarse and fine segmentation, are recognized using Tchebichef and Legendre moment invariants. In either coarse or fine segmented vehicle logo images, Tchebichef moment invariants perform better than the Legendre's. With the experimental accuracy results of 88.3% on the 240 dataset images of six different types of vehicle logos, it has demonstrated the effectiveness of the proposed method in recognizing the fine segmented vehicle logo, which supports the use of the system for real application.
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