5 February 2018 Extended color local mapped pattern for color texture classification under varying illumination
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Abstract
This paper presents a color–texture descriptor based on the local mapped pattern approach for color–texture classification under different lighting conditions. The proposed descriptor, namely extended color local mapped pattern (ECLMP), considers the magnitude of the color vectors inside the RGB cube to extract color–texture information from the images. These features are combined with texture information from the luminance image in a multiresolution fashion to get the ECLMP feature vector. The robustness of the proposed method is evaluated using the RawFooT, KTH-TIPS-2b, and USPtex databases. The experimental results show that the proposed descriptor is more robust to changes in the illumination condition than 22 alternative commonly used descriptors.
© 2018 SPIE and IS&T
Tamiris T. Negri, Fang Zhou, Zoran Obradovic, Adilson Gonzaga, "Extended color local mapped pattern for color texture classification under varying illumination," Journal of Electronic Imaging 27(1), 011008 (5 February 2018). https://doi.org/10.1117/1.JEI.27.1.011008 . Submission: Received: 29 September 2017; Accepted: 10 January 2018
Received: 29 September 2017; Accepted: 10 January 2018; Published: 5 February 2018
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