Texture Image Retrieval Using DTCWT-SVD and Local Binary Pattern Features


Dayou Jiang, Jongweon Kim, Journal of Information Processing Systems Vol. 13, No. 6, pp. 1628-1639, Dec. 2017  

https://doi.org/10.3745/JIPS.02.0077
Keywords: dual-tree complex wavelet transform, Image Retrieval, Local Binary Pattern, SVD, Texture Feature
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Abstract

The combination texture feature extraction approach for texture image retrieval is proposed in this paper. Two kinds of low level texture features were combined in the approach. One of them was extracted from singular value decomposition (SVD) based dual-tree complex wavelet transform (DTCWT) coefficients, and the other one was extracted from multi-scale local binary patterns (LBPs). The fusion features of SVD based multi-directional wavelet features and multi-scale LBP features have short dimensions of feature vector. The comparing experiments are conducted on Brodatz and Vistex datasets. According to the experimental results, the proposed method has a relatively better performance in aspect of retrieval accuracy and time complexity upon the existing methods.


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Cite this article
[APA Style]
Jiang, D. & Kim, J. (2017). Texture Image Retrieval Using DTCWT-SVD and Local Binary Pattern Features. Journal of Information Processing Systems, 13(6), 1628-1639. DOI: 10.3745/JIPS.02.0077.

[IEEE Style]
D. Jiang and J. Kim, "Texture Image Retrieval Using DTCWT-SVD and Local Binary Pattern Features," Journal of Information Processing Systems, vol. 13, no. 6, pp. 1628-1639, 2017. DOI: 10.3745/JIPS.02.0077.

[ACM Style]
Dayou Jiang and Jongweon Kim. 2017. Texture Image Retrieval Using DTCWT-SVD and Local Binary Pattern Features. Journal of Information Processing Systems, 13, 6, (2017), 1628-1639. DOI: 10.3745/JIPS.02.0077.