Shape classification using invariant features and contextual information in the bag-of-words model
A shape classifier tolerant to scale, rotation and viewpoint changes is proposed. The inclusion of the histogram of bi-grams improves the bag-of-words model. Codebook selection and feature learning improve the classification accuracy. We report best results on the animal shapes dataset using the proposed method.
shape classification contextual information bag-of-words codebook selection log-polar transform clustering evaluation entropy