Discriminative Weighting and Subspace Learning for Ensemble Symbol Recognition

Multi-scale parts-based models are particularly effective for recognizing non-segmented graphic symbols, i.e. the symbols interfered by other connecting or intersecting objects in the context. However, treating every symbol part and it features on every scale equally, despite some of them contributing little to the recognition, may lead to unnecessarily high dimensional representation of the symbol and affects the overall performance. In this paper, we propose a discriminant subspace learning and part weighting scheme for the parts-based ensemble symbol recognition model. The probabilistic vote on symbol category by each shape point of the symbol is adaptively weighted based on its surrounding context, and a more compact and representative description of the symbol is exploited based on the codebook learnt from the multi-scale shape context pyramids by K-SVD. The experiments demonstrate the effectiveness and adaptability of the proposed method on non-segmented intersecting symbols.