Multi-level contour combination features for shape recognition

We present a novel multi-level contour combination feature for shape recognition. This combination feature effectively solves large intra-class changes and nonlinear deformations of object shapes, thereby enhancing the performance of shape recognition. First, we divide the shape contour into two levels: the sampling points and the contour fragments, where sampling points are used to describe the detailed information of a shape and contour fragments are used to represent the global feature of a shape. Second, we employ the Fisher vector (FV) approach to encode the local sampling point feature and contour fragment feature as high-level characteristics. Finally, we combine the high-level characteristics after FV encoding and perform shape recognition through a linear support vector machine (SVM) model. The proposed method has been assessed on three benchmark shape datasets, including the Animal, MPEG-7,and ETH-80 datasets. Our method achieves 92.70%, 99.26% and 98.32% classification accuracy on the Animal, MPEG-7, and ETH-80 datasets, respectively. In addition, our method can also be applied to the classification of objects in real-word scenes. We combine the Weizmann Horse and the ETHZ Cow real-world scene datasets, and our method achieves 99.25% classification accuracy on the combined dataset. The recognition results of our approach are better than prior state-of-the-art shape recognition methods, which demonstrate the effectiveness and superiority of our approach. •We propose a novel multi-level contour combination feature method.•Our method solves the intra-class variation and complex deformation of a shape.•We are the first to study the shape from the multi-level perspective of contours.•The performance of our method exceeds the prior state-of-the-art approaches.