ε-Isometryε-Isometry based shape approximation for image content representation

Shape approximation is usually a prerequisite step to image content analysis and understanding and has been well studied in the passed decades. However, those approaches show their deficiencies while facing the factors such as the representation efficiency, the variation of image scale and the initial estimation. To alleviate these issues, we propose a novel method for ε-isometryε-isometry based shape approximation. We first analyze the descending property on approximating error and its relation with salient geometric features. After that, we approximate the polygonal shape and detect the feature point based on the ε-isometricε-isometric construction. In the experiments, we employ traditional shape benchmarks, MPEG7 shape dataset, SQUID dataset and other real contours to evaluate the visual effects and quantitative performances of the proposed method. Experimental results demonstrate that our method is not only robust to the initial estimation, but also outperforms the state-of-the-art methods with respect to the compactness and scale variability.

► The method provides a solution to the problem of polygonal approximation with ϵ-isometryϵ-isometry theory.
► The model enables a compact and scalable shape representation.
► The model outperforms, or at least comparable with related state-of-art research.