Measuring perceptual similarity and defining an appropriate similarity measure between trademark images remain largely unanswered. Most researchers used the Euclidean distance. This measure considers the difference in magnitude, rather than just the correlation of the features. We propose a new method based on cosine distance and normalized distance measures. The cosine distance metric normalizes all features vector to unit length and makes it invariant against relative in-plane scaling transformation of the image content. The normalized distance combines two distances measures such as cosine distance and Euclidean distance which shows more accuracy than one method alone. The proposed measures take into account the integration of global features (invariant moments and eccentricity) and local features (entropy histogram and distance histogram). It first indexes trademark images database (DB) in order to search for trademarks in narrowed limit and reduce time computation and then calculates similarities for features vector to obtain the total similarity between features vector. We have used retrieval efficiency equation in order to test the accuracy of our method. The obtained results showed that cosine distance and the normalization of cosine and Euclidean distance provide a significant improvement over the Euclidean distance.
['Beiji Zou', 'Marie Providence Umugwaneza']
Shape-Based Trademark Retrieval Using Cosine Distance Method