Integrating Contour and Skeleton for Shape Classification
Abstract
Shape analysis has been a long standing problem in the literature. In this paper, we address the shape classification problem and make the following contributions: (1) We combine both contour and skeleton (also local and global) information for shape analysis, and we derive an effective classifier. (2) We collect a challenging shape database in which there are 20 categories of animals, with each having 100 shapes. All these shapes are obtained from real images with a large variation in pose, viewing angle, articulation, and self-occlusion. (3) We emphasize the importance of having good representation for shape classification to address the unique characteristics of shape. A thorough experimental study is conducted showing significant improvement by the proposed algorithm over many of the state-of-the-art shape matching and classification algorithms, on both our dataset and the well-known MPEG-7 dataset. In addition, we applied our algorithm for recognizing and classifying objects from natural images and obtained very encouraging results.
Cite
Text
Bai et al. "Integrating Contour and Skeleton for Shape Classification." IEEE/CVF International Conference on Computer Vision Workshops, 2009. doi:10.1109/ICCVW.2009.5457679Markdown
[Bai et al. "Integrating Contour and Skeleton for Shape Classification." IEEE/CVF International Conference on Computer Vision Workshops, 2009.](https://mlanthology.org/iccvw/2009/bai2009iccvw-integrating/) doi:10.1109/ICCVW.2009.5457679BibTeX
@inproceedings{bai2009iccvw-integrating,
title = {{Integrating Contour and Skeleton for Shape Classification}},
author = {Bai, Xiang and Liu, Wenyu and Tu, Zhuowen},
booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
year = {2009},
pages = {360-367},
doi = {10.1109/ICCVW.2009.5457679},
url = {https://mlanthology.org/iccvw/2009/bai2009iccvw-integrating/}
}