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Efficient Color Texture Classification Using Color Monogenic Wavelet Transform

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Abstract

Color textures are among the most important visual attributes in image analysis. From the practical point view of color texture image analysis, this paper proposes an effective multi-scale color texture classification algorithm that is rotation and scale invariant using non-marginal color monogenic wavelet transform. The proposed algorithm exploits the color monogenic wavelet transform to obtain multi-scale representation of training samples for each texture class. The coefficients of color monogenic wavelet transform represent a magnitude and three phases: two phases encode local color information while the third contains geometric information of color texture image. The multi-scale feature vector is composed of mean value, standard deviation, energy and entropy at different scales of each of the directional sub-bands. The experimental results of average correct classification rates are 98.67, 99.08 and \(99.89\%\) which are obtained from different color texture databases demonstrate its superior performance and robustness of the proposed classifier. The proposed color texture feature vector is also shown to be effective for color texture classification.

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Acknowledgements

This work is partially supported by National Natural Science Foundation of China (61563037); Department of Education Science and Technology of Jiangxi Province under Grant No. (GJJ150755).

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Authors and Affiliations

  1. School of Information Engineering, Nanchang Hangkong University, Nanchang, 330063, Jiangxi, People’s Republic of China

    Shan Gai

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  1. Shan Gai
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Correspondence to Shan Gai.

Appendix: Clifford Algebra

Appendix: Clifford Algebra

The Clifford algebrais defined by the vector space V and quadratic form. Let \(\mathfrak {R}_{n,0} \) is non-commutative algebra generated by the vectors \(\left\{ {e_1 ,e_2 ,\ldots ,e_n } \right\} \). Given two vectors u and v in \(\mathfrak {R}^{n}\), then the geometric product uv is defined as follows:

$$\begin{aligned} uv=u\cdot v+u\wedge v \end{aligned}$$
(20)

where \(u\cdot v\) is the inner product and \(u\wedge v\) is the wedge product. This shows that \(\mathfrak {R}^{n}\) contains not only scalars and vectors but also bi-vectors and multi-vectors.

The vector space \(\mathfrak {R}_{n,0} \) is of dimension of \(2^{n}\) and its basis is given by \(\left\{ {e_i \left( {i=1,2,\ldots n} \right) } \right\} \). Then the Dirac operator that is defined on \(\mathfrak {R}_{n,0} \) is given by:

$$\begin{aligned} D=\sum _{k=1}^n {e_k \frac{\partial }{\partial x_k }} \end{aligned}$$
(21)

where for all \(i,j\in \left\{ {1,2,\ldots ,n} \right\} \), \(e_i e_j +e_j e_i =2\delta _{i,j} \), \(\delta _{i,j} \) is the delta function.

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Gai, S. Efficient Color Texture Classification Using Color Monogenic Wavelet Transform. Neural Process Lett 46, 609–626 (2017). https://doi.org/10.1007/s11063-017-9608-4

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