Coin image analysis, semantic evaluation and efficiency assessment

This paper presents a novel approach in coin image analysis based on middle-level features. The approach is used for image matching and it is developed utilizing edge pixel neighboring histograms. The overall structure of coin images are captured and used for similarity measurements. The closeness of the method to human perceptual judgment is evaluated by a semantic-based test along with the efficiency in comparison with three other methods known from the literature. The query set and the image database used in the tests are scanned, photographed, or collected from the Web. Experimental results show a significant supremacy of the new approach in semantic compatibility over the histogram of edge angles and the invariant moments. The proposed method is more efficient than the correlation method which is closer to human perception.