LBP-Motivated Colour Texture Classification
Abstract
In this paper we investigate extensions of Local Binary Patterns (LBP), Improved Local Binary Patterns (ILBP) and Extended Local Binary Patterns (ELBP) to colour textures via two different strategies: intra-/inter-channel features and colour orderings. We experimentally evaluate the proposed methods over 15 datasets of general and biomedical colour textures. Intra- and inter-channel features from the RGB space emerged as the best descriptors and we found that the best accuracy was achieved by combining multi-resolution intra-channel features with single-resolution inter-channel features.
Cite
Text
Bello-Cerezo et al. "LBP-Motivated Colour Texture Classification." European Conference on Computer Vision Workshops, 2018. doi:10.1007/978-3-030-11018-5_42Markdown
[Bello-Cerezo et al. "LBP-Motivated Colour Texture Classification." European Conference on Computer Vision Workshops, 2018.](https://mlanthology.org/eccvw/2018/bellocerezo2018eccvw-lbpmotivated/) doi:10.1007/978-3-030-11018-5_42BibTeX
@inproceedings{bellocerezo2018eccvw-lbpmotivated,
title = {{LBP-Motivated Colour Texture Classification}},
author = {Bello-Cerezo, Raquel and Fieguth, Paul W. and Bianconi, Francesco},
booktitle = {European Conference on Computer Vision Workshops},
year = {2018},
pages = {517-533},
doi = {10.1007/978-3-030-11018-5_42},
url = {https://mlanthology.org/eccvw/2018/bellocerezo2018eccvw-lbpmotivated/}
}