A Completed Modeling of Local Binary Pattern Operator for Texture Classification

By Zhenhua Guo, Lei Zhang, David Zhang. IEEE Transactions on Image Processing, 2010

2,023 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/53692881ac5b1c149adf6d22ccb2c414d7fdcf4e

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In Computer Vision and Pattern Recognition

Citations in the corpus

Persistence Images: A Stable Vector Representation of Persistent Homology (2015, methodology): “• When the natural rock and engraved surfaces are ﬁrst preprocessed using the completed local binary pattern (CLBP) operator for texture classiﬁciation (Guo et al., 2010), PIs outperform the aggregate descriptor.”
Learning Deep Face Representation (2014, background): “LTP [12], TPLBP, FPLBP [13], CLBP [14], LGBPHS [15], LE [16]).”
Gabor-Filtering-Based Completed Local Binary Patterns for Land-Use Scene Classification (2015, methodology): “Completed local binary patterns (CLBP) [20] operator, a complete modeling of the LBP operator, is then employed to extract the rotation invariant texture features (histograms) from the Gabor feature images as well as the input image.”
A Micro-GA Embedded PSO Feature Selection Approach to Intelligent Facial Emotion Recognition (2017, background): “Completed LBP (CLBP) [13] employs three key components, i.e., CLBP-center, CLBP-sign, and CLBP-magnitude, to extract the image’s local gray level and the sign and magnitude features of local difference, respectively.”
Remote Sensing Image Scene Classification Using Multi-Scale Completed Local Binary Patterns and Fisher Vectors (2016, methodology): “Since the CLBP [18] operator belongs to a gray-scale and rotation-invariant texture operator, the extracted local descriptors are robust to rotation image transformations.”
Rotation invariant co-occurrence features based on digital circles and discrete Fourier transform (2014, methodology): “The absence of tuning parameters, as well as the ease of implementation and other desirable asymptotic properties, support and explain the wide use of this method for comparative purposes (see for example Varma and Zisserman 2009; Guo et al. 2010; Crosier and Griffin 2010; Kandaswamy et al. 2011).”
Median Robust Extended Local Binary Pattern for Texture Classification (2016, methodology): “For the CUReT database we use the same subset of images which has been previously used in [12], [13], [41], [43]: 61 texture classes each with 92 images under varying illumination direction but at a constant scale.”
Gender-from-Iris or Gender-from-Mascara? (2017, methodology): “Local Binary Patterns (LBP) is a well-known method for texture analysis [10, 7].”
A Dataset for Breast Cancer Histopathological Image Classification (2016, methodology): “The CLBP is one of the latest variants of LBP is the CLBP [18], which provides a completed modeling of the LBP, based on three components extracted from the local region: center pixel, sign, and magnitude.”
Local Tetra Patterns: A New Feature Descriptor for Content-Based Image Retrieval (2012, methodology): “Guo et al. [20] used the magnitude component of the local difference operator to propose the magnitude LBP, along with the sign LBP for texture classification.”
Handcrafted vs. non-handcrafted features for computer vision classification (2017, methodology): “[42] disCLBP Discriminative Completed Local Binary Pattern.”
From BoW to CNN: Two Decades of Texture Representation for Texture Classification (2018, background): “[76], encoding not only the signs but also the magnitudes of local differences; and the Median Robust Extended LBP (MRELBP) of Liu et al.”
Extended local binary patterns for texture classification (2012, background): “Method Ours LBP/ VAR VZ-MR8 [2] VZ-Joint [3] VZ-MRF [3] CLBP [21]”
Land-use scene classification using multi-scale completed local binary patterns (2016, methodology): “Completed local binary patterns (CLBP) [20] descriptor is a completedmodeling of the LBP operator designed to achieve significant improvement over the LBP for rotation invariant texture classification.”
Texture Feature Extraction Methods: A Survey (2019, positive): “Furthermore, other variants have been developed to improve the discriminative power of the method [85]–[87], including multiscale approaches [88], [89].”