Shape classification using a new shape descriptor and multi-view learning

Abstract

Shape classification is considered as a vital task in solving many computer vision problems. Different factors such as affine transformations, scaling, rotations, variation in perspective, noise and occlusion have made the shape classification problem to be a hard problem. This work investigates a new shape descriptor that extracts different features from each boundary pixel. This makes it to be more informative and discriminant in comparison with other descriptors. After feature extraction, the “bag-of-features (BoF)” model is employed to construct the final representation for each image. To enhance the functionality of the BoF model, a novel codebook generation approach is presented. The proposed approach tends to derive a more meaningful visual codebook. Consequently, the produced feature vectors can handle inter- and intra-class variations more effectively. Comprehensive experiments conducted on the various complicated shape datasets show the supremacy of our approach compared to other methods.