ML Anthology

Using the Inner-Distance for Classification of Articulated Shapes

Haibin Ling, David W. Jacobs
CVPR 2005 pp. 719-726
doi:10.1109/CVPR.2005.362 /cvpr/2005/ling2005cvpr-using/

Abstract

We propose using the inner-distance between landmark points to build shape descriptors. The inner-distance is defined as the length of the shortest path between landmark points within the shape silhouette. We show that the inner-distance is articulation insensitive and more effective at capturing complex shapes with part structures than Euclidean distance. To demonstrate this idea, it is used to build a new shape descriptor based on shape contexts. After that, we design a dynamic programming based method for shape matching and comparison. We have tested our approach on a variety of shape databases including an articulated shape dataset, MPEG7 CE-Shape-1, Kimia silhouettes, a Swedish leaf database and a human motion silhouette dataset. In all the experiments, our method demonstrates effective performance compared with other algorithms.

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Cite

Text

Ling and Jacobs. "Using the Inner-Distance for Classification of Articulated Shapes." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2005. doi:10.1109/CVPR.2005.362

Markdown

[Ling and Jacobs. "Using the Inner-Distance for Classification of Articulated Shapes." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2005.](https://mlanthology.org/cvpr/2005/ling2005cvpr-using/) doi:10.1109/CVPR.2005.362

BibTeX

@inproceedings{ling2005cvpr-using,
  title     = {{Using the Inner-Distance for Classification of Articulated Shapes}},
  author    = {Ling, Haibin and Jacobs, David W.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2005},
  pages     = {719-726},
  doi       = {10.1109/CVPR.2005.362},
  url       = {https://mlanthology.org/cvpr/2005/ling2005cvpr-using/}
}