ML Anthology

Fusing Image and Segmentation Cues for Skeleton Extraction in the Wild

Xiaolong Liu, Pengyuan Lyu, Xiang Bai, Ming-Ming Cheng
ICCVW 2017 pp. 1744-1748
doi:10.1109/ICCVW.2017.205 /iccvw/2017/liu2017iccvw-fusing/

Abstract

Extracting skeletons from natural images is a challenging problem, due to complex backgrounds in the scene and various scales of objects. To address this problem, we propose a two-stream fully convolutional neural network which uses the original image and its corresponding semantic segmentation probability map as inputs and predicts the skeleton map using merged multi-scale features. We find that the semantic segmentation probability map is complementary to the corresponding color image and can boost the performance of our baseline model which trained only on color images. We conduct experiments on SK-LARGE dataset and the F-measure of our method on validation set is 0.738 which outperforms current state-of-the-art significantly and demonstrates the effectiveness of our proposed approach.

ICCVW Semantic Scholar

Cite

Text

Liu et al. "Fusing Image and Segmentation Cues for Skeleton Extraction in the Wild." IEEE/CVF International Conference on Computer Vision Workshops, 2017. doi:10.1109/ICCVW.2017.205

Markdown

[Liu et al. "Fusing Image and Segmentation Cues for Skeleton Extraction in the Wild." IEEE/CVF International Conference on Computer Vision Workshops, 2017.](https://mlanthology.org/iccvw/2017/liu2017iccvw-fusing/) doi:10.1109/ICCVW.2017.205

BibTeX

@inproceedings{liu2017iccvw-fusing,
  title     = {{Fusing Image and Segmentation Cues for Skeleton Extraction in the Wild}},
  author    = {Liu, Xiaolong and Lyu, Pengyuan and Bai, Xiang and Cheng, Ming-Ming},
  booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
  year      = {2017},
  pages     = {1744-1748},
  doi       = {10.1109/ICCVW.2017.205},
  url       = {https://mlanthology.org/iccvw/2017/liu2017iccvw-fusing/}
}