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Texture Classification Using Scattering Statistical and Cooccurrence Features

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  • Juan Wang
  • Jiangshe Zhang
  • Jie Zhao

Abstract

Texture classification is an important research topic in image processing. In 2012, scattering transform computed by iterating over successive wavelet transforms and modulus operators was introduced. This paper presents new approaches for texture features extraction using scattering transform. Scattering statistical features and scattering cooccurrence features are derived from subbands of the scattering decomposition and original images. And these features are used for classification for the four datasets containing 20, 30, 112, and 129 texture images, respectively. Experimental results show that our approaches have the promising results in classification.

Suggested Citation

  • Juan Wang & Jiangshe Zhang & Jie Zhao, 2016. "Texture Classification Using Scattering Statistical and Cooccurrence Features," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-6, February.
    • Handle: RePEc:hin:jnlmpe:3946312
    DOI: 10.1155/2016/3946312
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    Cited by:

    1. Sachin Kumar & Shivam Panwar & Jagvinder Singh & Anuj Kumar Sharma & Zairu Nisha, 2022. "iCACD: an intelligent deep learning model to categorise current affairs news article for efficient journalistic process," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(5), pages 2572-2582, October.

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