Local binary features for texture classification: Taxonomy and experimental study
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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
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Self-citation
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Sorted by citations
Deep Learning for Generic Object Detection: A Survey
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Local feature extraction based facial emotion recognition: a survey
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“[48] provided a systematic review of LBP variants while regrouping them into different categories.”
Background
- 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
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