TEXTURE CLASSIFICATION USING SPARSE K-SVD TEXTON DICTIONARIES

Muhammad Rushdi, Jeffrey Ho

2011

Abstract

This paper addresses the problem of texture classification under unknown viewpoint and illumination variations. We propose an approach that combines sparse K-SVD and texton-based representations. Starting from an analytic or data-driven base dictionary, a sparse dictionary is iteratively estimated from the texture data using the doubly-sparse K-SVD algorithm. Then, for each texture image, K-SVD representations of pixel neighbourhoods are computed and used to assign the pixels to textons. Hence, the texture image is represented by the histogram of its texton map. Finally, a test image is classified by finding the closest texton histogram using the chi-squared distance. Initial experiments on the CUReT database show high classification rates that compare well with Varma-Zisserman MRF results.

Paper Citation

in Harvard Style

Rushdi M. and Ho J. (2011). TEXTURE CLASSIFICATION USING SPARSE K-SVD TEXTON DICTIONARIES . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2011) ISBN 978-989-8425-47-8, pages 187-193. DOI: 10.5220/0003376101870193

in Bibtex Style

@conference{visapp11,
author={Muhammad Rushdi and Jeffrey Ho},
title={TEXTURE CLASSIFICATION USING SPARSE K-SVD TEXTON DICTIONARIES},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2011)},
year={2011},
pages={187-193},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003376101870193},
isbn={978-989-8425-47-8},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2011)
TI - TEXTURE CLASSIFICATION USING SPARSE K-SVD TEXTON DICTIONARIES
SN - 978-989-8425-47-8
AU - Rushdi M.
AU - Ho J.
PY - 2011
SP - 187
EP - 193
DO - 10.5220/0003376101870193