The DOI matches its Crossref record; 70 of the 70 references it cites were found in a registry or an index.
Pairwise Rotation Invariant Co-Occurrence Local Binary Pattern
Abstract
Designing effective features is a fundamental problem in computer vision. However, it is usually difficult to achieve a great tradeoff between discriminative power and robustness. Previous works shown that spatial co-occurrence can boost the discriminative power of features. However the current existing co-occurrence features are taking few considerations to the robustness and hence suffering from sensitivity to geometric and photometric variations. In this work, we study the Transform Invariance (TI) of co-occurrence features. Concretely we formally introduce a Pairwise Transform Invariance (PTI) principle, and then propose a novel Pairwise Rotation Invariant Co-occurrence Local Binary Pattern (PRICoLBP) feature, and further extend it to incorporate multi-scale, multi-orientation, and multi-channel information. Different from other LBP variants, PRICoLBP can not only capture the spatial context co-occurrence information effectively, but also possess rotation invariance. We evaluate PRICoLBP comprehensively on nine benchmark data sets from five different perspectives, e.g., encoding strategy, rotation invariance, the number of templates, speed, and discriminative power compared to other LBP variants. Furthermore we apply PRICoLBP to six different but related applications-texture, material, flower, leaf, food, and scene classification, and demonstrate that PRICoLBP is efficient, effective, and of a well-balanced tradeoff between the discriminative power and robustness.
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Cites
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Textural Features for Image Classification
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Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
Oliva, A., & Torralba, A. (2001) · International Journal of Computer Vision · doi:10.1023/a:1011139631724 · Cited by 6,319
Face Description with Local Binary Patterns: Application to Face Recognition
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Automated Flower Classification over a Large Number of Classes
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SUN database: Large-scale scene recognition from abbey to zoo
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Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions
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Yang, M., Zhang, L., Feng, X., & Zhang, D. (2014) · International Journal of Computer Vision · doi:10.1007/s11263-014-0722-8 · Cited by 489
Median Robust Extended Local Binary Pattern for Texture Classification
Liu, L., Lao, S., Fieguth, P. W., Guo, Y., Wang, X., & Pietikäinen, M. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2522378 · Cited by 431
Local 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
Deep Filter Banks for Texture Recognition, Description, and Segmentation
Cimpoi, M., Maji, S., Kokkinos, I., & Vedaldi, A. (2016) · International Journal of Computer Vision · doi:10.1007/s11263-015-0872-3 · Cited by 335
Generalized 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
- Qi, X., Xiao, R., Li, C.-G., Qiao, Y., Guo, J., & Tang, X.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2014-11-01
- Volume
- 36
- Issue
- 11
- Pages
- 2199-2213
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- OpenAlex
- W2053544201
- Cited by
- 296
- References
- 70
- FWCI
- 33.83
- Type
- journal article
- Language
- en
- Open access
- closed
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