5 December 2017 Improved opponent color local binary patterns: an effective local image descriptor for color texture classification
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Abstract
Texture classification plays a major role in many computer vision applications. Local binary patterns (LBP) encoding schemes have largely been proven to be very effective for this task. Improved LBP (ILBP) are conceptually simple, easy to implement, and highly effective LBP variants based on a point-to-average thresholding scheme instead of a point-to-point one. We propose the use of this encoding scheme for extracting intra- and interchannel features for color texture classification. We experimentally evaluated the resulting improved opponent color LBP alone and in concatenation with the ILBP of the local color contrast map on a set of image classification tasks over 9 datasets of generic color textures and 11 datasets of biomedical textures. The proposed approach outperformed other grayscale and color LBP variants in nearly all the datasets considered and proved competitive even against image features from last generation convolutional neural networks, particularly for the classification of biomedical images.
© 2017 SPIE and IS&T
Francesco Bianconi, Raquel Bello-Cerezo, Paolo Napoletano, "Improved opponent color local binary patterns: an effective local image descriptor for color texture classification," Journal of Electronic Imaging 27(1), 011002 (5 December 2017). https://doi.org/10.1117/1.JEI.27.1.011002 . Submission: Received: 17 June 2017; Accepted: 6 September 2017
Received: 17 June 2017; Accepted: 6 September 2017; Published: 5 December 2017
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