Rotation invariant texture classification using LBP variance (LBPV) with global matching

By Zhenhua Guo, Lei Zhang, David Dapeng Zhang. Pattern Recognition, 2010

849 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/c8ea49c1b96886d02965acbabc25227ce6254422

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In Computer Vision and Pattern Recognition

Citations in the corpus

Pairwise Rotation Invariant Co-Occurrence Local Binary Pattern (2014, methodology): “Unfortunately the current existing works on co-occurrence features mainly focus on improving discriminativeness, e.g., [8], [9], [12], [14], [15], [16], [21], [22].”
Extended local binary patterns for texture classification (2012, background): “[4] and with other LBP based approaches [21, 33, 20].”
DOCToR: The Role of Deep Features in Content-Based Mammographic Image Retrieval (2018, methodology): “Feature Extractor Category #Features BIC Histogram [15] Color 512 Edge Histogram [16] Color 150 Normalized Histogram [17] Color 256 Haralick [18] Texture 24 Rotation Invariant LBP [19] Texture 108 Texture Spectrum [20] Texture 8 Daubechies [21] Shape 16 Haar [21] Shape 16 Zernike [22] Shape 36”
Presentation Attack Detection Methods for Face Recognition Systems (2017, background): “Furthermore, the use of other LBP variants such as LBP variance (LBPV) [Zhenhua et al. 2010], Contrast LBP [Guo et al. 2010], and Center- Symmetric-LBP [Heikkiï a et al. 2006] were also evaluated for both the photo and display screen attack in Raghavendra et al. [2015].”
Median Robust Extended Local Binary Pattern for Texture Classification (2016, methodology): “For the overall framework of the proposed approach, the actual classification is performed via the simple Nearest Neighbor Classifier (NNC), applied to the normalized MRELBP histogram feature vectors, using the χ2 distance metric as in [12], [43], and [46].”
Fusion of Deep Learning and Compressed Domain Features for Content-Based Image Retrieval (2017, background): “Conversely, the second case exploits low-level features such as color, edges and textures, and it is well suited for the practical application, and has become much more popular than the first type of retrieval methods [21]–[57].”