A new system for trademark segmentation and retrieval

Abstract

Abstract With the increase in the number of trademarks, trademark imitation has become a serious problem. Thus, building an efficient trademark retrieval system is imperative. In this paper, such a system is presented. First, a semi-automatic segmentation method is proposed to extract the shapes of those representative objects, called ‘masks’, in each trademark. Next, some features are selected to describe a mask. These include invariant moments, the histogram of edge directions, and two kinds of transform coefficients that are robust to geometric deformation. Then, based on the rank of the feature distance, a similarity measure is provided to do the similar trademark retrieval. Finally, a feedback algorithm is also proposed to automatically determine the weight of each feature according to the user's response. Furthermore, in order to show the effectiveness of the proposed system, two databases from MPEG-7 test database are used to compare the performances of the proposed system and those methods using chain code, Zernike moments or MPLV as features. The experimental results show that the proposed system is superior to others.