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Texture classification with cross-covariance matrices in compressive measurement domain

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Abstract

Texture classification of images is an important problem in texture analysis, with the goal to assign an unknown sample image to one of a set of known texture classes. Many procedures have been proposed. However, traditional texture classification methods are often complicated and time-consuming. In addition, the whole original data set has to be known in advance. This paper presents a novel approach to texture classification that employs cross-covariance matrices in the compressive measurement domain. Since there is a linear relation between the cross-covariance matrix in the measurement domain and that in the transform domain, the characteristics of the original signals can be reflected through the sampling measurements. By abstracting the measurement-domain cross-covariance matrices of each image class, we build a texton dictionary composed of the cross-covariance vectors and then train each image class according to the texton dictionary. Through comparison with all the training results, test images are classified as the matching one. Experiments show that the texture classification method proposed in this paper has better accuracy, with only 14–50 % processing time than the existing methods.

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Acknowledgments

This work has been supported by the National Natural Science Foundation of China (61271173, 61372068), the Research Fund for the Doctoral Program of Higher Education of China (20130203110005), the Fundamental Research Funds for the Central Universities (K5051301033), the 111 Project (B08038), and also supported by ISN State Key Laboratory.

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Authors and Affiliations

  1. State Key Laboratory of Integrated Services Networks, Xidian University, No.2 South Taibai Road, Xi’an, China

    Jie Guo, Bin Song, Fang Tian & Hao Qin

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  1. Jie Guo
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  2. Bin Song
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  3. Fang Tian
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  4. Hao Qin
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Correspondence to Bin Song.

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Guo, J., Song, B., Tian, F. et al. Texture classification with cross-covariance matrices in compressive measurement domain. SIViP 10, 1377–1384 (2016). https://doi.org/10.1007/s11760-016-0902-9

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  • DOI: https://doi.org/10.1007/s11760-016-0902-9

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