Dominant Local Binary Patterns for Texture Classification

By Shu Liao, Max W. K. Law, Albert C. S. Chung. IEEE Transactions on Image Processing, 2009

845 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/0dfe6498b0db027b049d9a658c1d8ab9a504c10b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Li Liu
2. Xiaogang Wang
3. Paul W. Fieguth
4. Matti Pietikäinen
5. Wei Li
6. Dacheng Tao
7. Xiaoou Tang
8. Guoying Zhao

Citations in the corpus

• Extended local binary patterns for texture classification (2012, background): “It is suggested in [20] that DLBP in combination with another complementary Gabor based descriptor NGF, which captures global texture information, can yield improved and robust classification results.”
• Rotation-Invariant Image and Video Description With Local Binary Pattern Features (2012, background): “Despite being simple, it is very descriptive, which is attested by the wide variety of different tasks it has been successfully applied to.”
• Local Directional Number Pattern for Face Analysis: Face and Expression Recognition (2013, unknown): “Thus, we obtain a set of masks similar to those shown in Fig.”
• A Micro-GA Embedded PSO Feature Selection Approach to Intelligent Facial Emotion Recognition (2017, background): “According to [12], uniform patterns in LBP can lead to a loss of information with respect to complex shapes despite their effectiveness in capturing fundamental patterns in an input image.”
• A Completed Modeling of Local Binary Pattern Operator for Texture Classification (2010, unknown): “Although some variants of LBP, such as derivative-based LBP [17], dominant LBP [22] and center-symmetric LBP [23], have been proposed recently, there still remain some questions to be better answered for LBP.”
• Extended local binary patterns for face recognition (2016, methodology): “(4) For each subregion, apply a feature extraction operator ( e.g. ADLBP_S) to each pixel. the feature values and concatenate these histograms into a single feature vector to represent the face image.”
• Local Tetra Patterns: A New Feature Descriptor for Content-Based Image Retrieval (2012, background): “Ojala et al. proposed LBPs [15] , which are converted to a rotational invariant version for texture classification [16], [17].”
• Local Binary Patterns and Extreme Learning Machine for Hyperspectral Imagery Classification (2015, background): “The locations of circular neighbors that do not fall exactly on image grids are estimated by bilinear interpolation [38].”
• Texture Classification from Random Features (2012, methodology): “The Brodatz small data set D b (24 classes) was chosen to allow a direct comparison with the state-of-the-art results from [55].”
• Median Robust Extended Local Binary Pattern for Texture Classification (2016, background): “[14] introduced Dominant LBP (DLBP) to learn the most frequently occurred patterns to capture descriptive textural information, but which requires pretraining.”