CollageParsing: Nonparametric Scene Parsing by Adaptive Overlapping Windows
Abstract
Scene parsing is the problem of assigning a semantic label to every pixel in an image. Though an ambitious task, impressive advances have been made in recent years, in particular in scalable nonparametric techniques suitable for open-universe databases. This paper presents the CollageParsing algorithm for scalable nonparametric scene parsing. In contrast to common practice in recent nonparametric approaches, CollageParsing reasons about mid-level windows that are designed to capture entire objects, instead of low-level superpixels that tend to fragment objects. On a standard benchmark consisting of outdoor scenes from the LabelMe database, CollageParsing achieves state-of-the-art nonparametric scene parsing results with 7 to 11% higher average per-class accuracy than recent nonparametric approaches.
Cite
Text
Tung and Little. "CollageParsing: Nonparametric Scene Parsing by Adaptive Overlapping Windows." European Conference on Computer Vision, 2014. doi:10.1007/978-3-319-10599-4_33Markdown
[Tung and Little. "CollageParsing: Nonparametric Scene Parsing by Adaptive Overlapping Windows." European Conference on Computer Vision, 2014.](https://mlanthology.org/eccv/2014/tung2014eccv-collageparsing/) doi:10.1007/978-3-319-10599-4_33BibTeX
@inproceedings{tung2014eccv-collageparsing,
title = {{CollageParsing: Nonparametric Scene Parsing by Adaptive Overlapping Windows}},
author = {Tung, Frederick and Little, James J.},
booktitle = {European Conference on Computer Vision},
year = {2014},
pages = {511-525},
doi = {10.1007/978-3-319-10599-4_33},
url = {https://mlanthology.org/eccv/2014/tung2014eccv-collageparsing/}
}