Partial Similarity Based Nonparametric Scene Parsing in Certain Environment
Abstract
In this paper we propose a novel nonparametric image parsing method for the image parsing problem in certain environment. A novel and efficient nearest neighbor matching scheme, the ANN bilateral matching scheme, is proposed. Based on the proposed matching scheme, we first retrieve some partially similar images for each given test image from the training image database. The test image can be well explained by these retrieved images, with similar regions existing in the retrieved images for each region in the test image. Then, we match the test image to the retrieved training images with the ANN bilateral matching scheme, and parse the test image by integrating multiple cues in a markov random field. Experiment on three datasets shows our method achieved promising parsing accuracy and outperformed two state-of-the-art nonparametric image parsing methods.
Cite
Text
Zhang et al. "Partial Similarity Based Nonparametric Scene Parsing in Certain Environment." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2011. doi:10.1109/CVPR.2011.5995348Markdown
[Zhang et al. "Partial Similarity Based Nonparametric Scene Parsing in Certain Environment." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2011.](https://mlanthology.org/cvpr/2011/zhang2011cvpr-partial/) doi:10.1109/CVPR.2011.5995348BibTeX
@inproceedings{zhang2011cvpr-partial,
title = {{Partial Similarity Based Nonparametric Scene Parsing in Certain Environment}},
author = {Zhang, Honghui and Fang, Tian and Chen, Xiaowu and Zhao, Qinping and Quan, Long},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2011},
pages = {2241-2248},
doi = {10.1109/CVPR.2011.5995348},
url = {https://mlanthology.org/cvpr/2011/zhang2011cvpr-partial/}
}