Shape classification using invariant features and contextual information in the bag-of-words model

作者：

Highlights：

• 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.

摘要

Highlights•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.

论文关键词：Shape classification,Bag-of-words,Log-polar transform,Contextual information,Codebook selection,Clustering evaluation,Entropy

论文评审过程：Received 22 October 2013, Revised 15 July 2014, Accepted 17 September 2014, Available online 30 September 2014.

论文官网地址：https://doi.org/10.1016/j.patcog.2014.09.019