Proceedings of the 2017 6th International Conference on Measurement, Instrumentation and Automation (ICMIA 2017)

Space and Contourlet Domains Texture Image Retrieval Algorithm

Authors
Xinwu Chen, Jingjing Xue, Li Zhang, Shuangbo Xie, Peng Wang
Corresponding Author
Xinwu Chen
Available Online June 2017.
DOI
https://doi.org/10.2991/icmia-17.2017.72How to use a DOI?
Keywords
texture image retrieval; contourlet transform; standard deviation; L1-energy; L2-energy; local oriented statistics information booster.
Abstract
Contourlet transform has been widely used in many image processing applications including digital image denoising, texture image retrieval, etc. When contourlet transform is used for texture image retrieval problems, features like standard deviation, skewness, kurtosis, L1-energy, L2-energy and others of contourlet subbands was used to characterize the nature of textures in digital images. Many other methods like local binary patterns are often used to construct texture image retrieval systems. In this work, we combine features from traditional approaches with local oriented statistics information booster (LOSIB) to improve the texture image retrieval rates. Experiments on Brodatz texture image database was carried out, and the results show that the combination of the features from contourlet and space domains can improve the results efficiently.
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Proceedings
2017 6th International Conference on Measurement, Instrumentation and Automation (ICMIA 2017)
Part of series
Advances in Intelligent Systems Research
Publication Date
June 2017
ISBN
978-94-6252-387-6
ISSN
1951-6851
DOI
https://doi.org/10.2991/icmia-17.2017.72How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Xinwu Chen
AU  - Jingjing Xue
AU  - Li Zhang
AU  - Shuangbo Xie
AU  - Peng Wang
PY  - 2017/06
DA  - 2017/06
TI  - Space and Contourlet Domains Texture Image Retrieval Algorithm
BT  - 2017 6th International Conference on Measurement, Instrumentation and Automation (ICMIA 2017)
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/icmia-17.2017.72
DO  - https://doi.org/10.2991/icmia-17.2017.72
ID  - Chen2017/06
ER  -