Trademark classification by shape using ensemble of RBFNNs

Identification of similar trademarks is important in trademark registration. Shape feature could intuitively and effectively describes an object in a given image. Therefore, shape feature plays an important role in content-based image retrieval (CBIR) systems. The shape feature is particularly suitable for trademark image retrieval (TIR) systems. In this paper, we propose an effective solution for TIR by using Hu´s invariant moments and an ensemble of Radial Basis Function Neural Networks (RBFNN) trained via a minimization of the Localized Generalization Error Model (L-GEM). The proposed method outperforms TIR with similarity measure based on Euclidean distance.