The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.
Median Robust Extended Local Binary Pattern for Texture Classification
Abstract
Local binary patterns (LBP) are considered among the most computationally efficient high-performance texture features. However, the LBP method is very sensitive to image noise and is unable to capture macrostructure information. To best address these disadvantages, in this paper, we introduce a novel descriptor for texture classification, the median robust extended LBP (MRELBP). Different from the traditional LBP and many LBP variants, MRELBP compares regional image medians rather than raw image intensities. A multiscale LBP type descriptor is computed by efficiently comparing image medians over a novel sampling scheme, which can capture both microstructure and macrostructure texture information. A comprehensive evaluation on benchmark data sets reveals MRELBP's high performance-robust to gray scale variations, rotation changes and noise-but at a low computational cost. MRELBP produces the best classification scores of 99.82%, 99.38%, and 99.77% on three popular Outex test suites. More importantly, MRELBP is shown to be highly robust to image noise, including Gaussian noise, Gaussian blur, salt-and-pepper noise, and random pixel corruption.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
Ojala, T., Pietikainen, M., & Maenpaa, T. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2002.1017623 · Cited by 15,398
Robust Face Recognition via Sparse Representation
Wright, J., Yang, A. Y., Ganesh, A., Sastry, S. S., & Ma, Y. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.79 · Cited by 9,388
Face Description with Local Binary Patterns: Application to Face Recognition
Ahonen, T., Hadid, A., & Pietikainen, M. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.244 · Cited by 5,584
BRISK: Binary Robust invariant scalable keypoints
Leutenegger, S., Chli, M., & Siegwart, R. Y. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126542 · Cited by 3,450
Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions
Zhao, G., & Pietikainen, M. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1110 · Cited by 2,846
Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions
Tan, X., & Triggs, B. (2010) · IEEE Transactions on Image Processing · doi:10.1109/tip.2010.2042645 · Cited by 2,842
A Completed Modeling of Local Binary Pattern Operator for Texture Classification
Guo, Z., Zhang, L., & Zhang, D. (2010) · IEEE Transactions on Image Processing · doi:10.1109/tip.2010.2044957 · Cited by 2,115
Zhang, J., Marszałek, M., Lazebnik, S., & Schmid, C. (2007) · International Journal of Computer Vision · doi:10.1007/s11263-006-9794-4 · Cited by 1,923
Alahi, A., Ortiz, R., & Vandergheynst, P. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6247715 · Cited by 1,775
DAISY: An Efficient Dense Descriptor Applied to Wide-Baseline Stereo
Tola, E., Lepetit, V., & Fua, P. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.77 · Cited by 1,405
Cited by
Handcrafted vs. non-handcrafted features for computer vision classification
Nanni, L., Ghidoni, S., & Brahnam, S. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.05.025 · Cited by 542
Texture Feature Extraction Methods: A Survey
Humeau-Heurtier, A. (2019) · IEEE Access · doi:10.1109/access.2018.2890743 · Cited by 521
From BoW to CNN: Two Decades of Texture Representation for Texture Classification
Liu, L., Chen, J., Fieguth, P., Zhao, G., Chellappa, R., & Pietikäinen, M. (2019) · International Journal of Computer Vision · doi:10.1007/s11263-018-1125-z · Cited by 375
Self-citationLocal binary features for texture classification: Taxonomy and experimental study
Liu, L., Fieguth, P., Guo, Y., Wang, X., & Pietikäinen, M. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2016.08.032 · Cited by 369
Self-citationGeneralized Completed Local Binary Patterns for Time-Efficient Steel Surface Defect Classification
Luo, Q., Sun, Y., Li, P., Simpson, O., Tian, L., & He, Y. (2019) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2018.2852918 · Cited by 178
Metadata
- Authors
- Liu, L., Lao, S., Fieguth, P. W., Guo, Y., Wang, X., & Pietikäinen, M.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2016-03
- Volume
- 25
- Issue
- 3
- Pages
- 1368-1381
- ISSN
- 1057-7149 (ISSN-L)
- 1941-0042
- OpenAlex
- W2293457052
- Cited by
- 431
- References
- 53
- FWCI
- 36.05
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Median Robust Extended Local Binary Pattern for Texture Classification. https://app.cochat.ai/papers/10.1109/tip.2016.2522378
- The paper
- Liu, L., Lao, S., Fieguth, P. W., Guo, Y., Wang, X., & Pietikäinen, M. (2016). Median Robust Extended Local Binary Pattern for Texture Classification. IEEE Transactions on Image Processing. https://doi.org/10.1109/tip.2016.2522378
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
