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Extended Gaussian-Filtered Local Binary Patterns for Colonoscopy Image Classification
AbstractLocal Binary Patterns (LBP) and its variants are widely used for texture classification. In this paper we propose a new variant of LBP descriptor called the extended Gaussian filtered Local Binary Patterns (xGF-LBP) which is robust to illumination changes, noise and captures more informative edge-like features for classification. Experiments on a colonoscopy image dataset with 2100 images for binary ('normal' or 'abnormal') classification show that the proposed xGF-LBP descriptor significantly outperforms the standard LBP descriptor and its considered variants.
Related Material
[pdf][bibtex]@InProceedings{Manivannan_2013_ICCV_Workshops,
author = {Siyamalan Manivannan and Ruixuan Wang and Emanuele Trucco},
title = {Extended Gaussian-Filtered Local Binary Patterns for Colonoscopy Image Classification},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops},
month = {June},
year = {2013}
}
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