Curvature Bag of Words Model for Shape Recognition

Abstract

The object shape recognition of nonrigid transformations and local deformations is a difficult problem. In this paper, a shape recognition algorithm based on the curvature bag of words (CBoW) model is proposed to solve that problem. First, an approximate polygon of the object contour is obtained by using the discrete contour evolution algorithm. Next, based on the polygon vertices, the shape contour is decomposed into contour fragments. Then, the CBoW model is used to represent the contour fragments. Finally, a linear support vector machine is applied to classify the shape feature descriptors. Our main innovations are as follows: 1) A multi-scale curvature integral descriptor is proposed to extend the representativeness of the local descriptor; 2) The curvature descriptor is encoded to break through the limitation of the correspondence relationship of the sampling points for shape matching, and accordingly it forms the feature of middle-level semantic description; 3) The equal-curvature integral ranking pooling is employed to enhance the feature discrimination, and also improves the performance of the middle-level descriptor. The experimental results show that the recognition rate of the proposed algorithm in the MPEG-7 database can reach 98.21%. The highest recognition rates of the Swedish Leaf and the Tools databases are 97.23% and 97.14%, respectively. The proposed algorithm achieves a high recognition rate and has good robustness, which can be applied to the target shape recognition field for nonrigid transformations and local deformations.