Cited by 369Image Retrieval and Classification TechniquesRadiomics and Machine Learning in Medical ImagingAdvanced Image and Video Retrieval Techniques

Local binary features for texture classification: Taxonomy and experimental study

Liu, Li, Fieguth, Paul, Guo, Yulan, Wang, Xiaogang, Pietikäinen, Matti·2017·Pattern Recognition

Integrity signals

Registry

Crossref · 10.1016/j.patcog.2016.08.032

Cites

131 of 131 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • DCNNs are inherently limited by the lack of ability to be spatially invariant to geometric transformations of the input data.

    “DCNNs are inherently limited by the lack of ability to be spatially invariant to geometric transformations of the input data (Lenc and Vedaldi 2018;Liu et al. 2017;Chellappa 2016).”

    From Deep Learning for Generic Object Detection: A Survey (2020) · cites this paper as Liu et al. 2017

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Liu, L., Fieguth, P., Guo, Y., Wang, X., & Pietikäinen, M.
Affiliations
  • National University of Defense Technology (CN)
  • University of Waterloo (CA)
  • Chinese University of Hong Kong (HK)
  • University of Oulu (FI)
Corresponding author
Liu, Li
Venue
Pattern Recognition
Publisher
Elsevier BV
Published
2017-02
Volume
62
Pages
135-160
ISSN
0031-3203 (ISSN-L)
OpenAlex
W2518260411
Cited by
369
References
131
Citations by year
2026: 13 · 2025: 18 · 2024: 25 · 2023: 25 · 2022: 30 · 2021: 43 · 2020: 52 · 2019: 74 · 2018: 63 · 2017: 26
FWCI
32.77
Type
journal article
Language
en
Open access
closed
Locations
Pattern Recognition (publishedVersion)
Funders
  • National Natural Science Foundation of China (61202336, 61601481)
  • Open Project Program of the National Laboratory of Pattern Recognition (NLPR)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
Keywords
local binary pattern, texture classification, local texture descriptors, deep convolutional neural networks, computational complexity

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