27 February 2018 Multi-color space local binary pattern-based feature selection for texture classification
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Abstract
This paper deals with multi-color space texture classification. Two approaches are proposed and compared: a multi-color space histogram selection (MCSHS) and a multi-color space bin selection. These approaches select local binary pattern (LBP) histograms or LBP bins that have been processed from images coded in multiple color spaces. On the one hand, the proposed LBP-based feature selection scheme overcomes the difficulty of choosing a relevant color space, and on the other hand, it takes advantage of the specific properties of several color spaces by combining them. Experiments show that the MCSHS approach is relevant for color texture classification issues that require good performances whether in accuracy or classification computation time.
© 2018 SPIE and IS&T
Alice Porebski, Alice Porebski, Vinh Truong Hoang, Vinh Truong Hoang, Nicolas Vandenbroucke, Nicolas Vandenbroucke, Denis Hamad, Denis Hamad, } "Multi-color space local binary pattern-based feature selection for texture classification," Journal of Electronic Imaging 27(1), 011010 (27 February 2018). https://doi.org/10.1117/1.JEI.27.1.011010 . Submission: Received: 20 December 2017; Accepted: 2 February 2018
Received: 20 December 2017; Accepted: 2 February 2018; Published: 27 February 2018
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