Abstract
This chapter focuses on another image feature called the texture feature. Two types of texture feature methods are discussed: traditional spatial methods and contemporary spectral methods. The chapter first introduces four spatial or handcrafted methods including Tamura, GLCM, MRF, and FD . Readers are shown how microstructures of an image can be modeled with handcraft and statistical methods. Pros and cons of spatial methods are also discussed. In the next, four spectral texture methods are described in details including DCT , Gabor, wavelet, and curvelet . These spectral texture methods not only match theory with data but also strengthen readers’ understanding of the powerful spectral transforms introduced in Part I. Readers are demonstrated with rich working examples and illustrations.
The devil is in the detail.
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Zhang, D. (2019). Texture Feature Extraction. In: Fundamentals of Image Data Mining. Texts in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-030-17989-2_5
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DOI: https://doi.org/10.1007/978-3-030-17989-2_5
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