The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Rotation-Invariant Image and Video Description With Local Binary Pattern Features
Abstract
In this paper, we propose a novel approach to compute rotation-invariant features from histograms of local noninvariant patterns. We apply this approach to both static and dynamic local binary pattern (LBP) descriptors. For static-texture description, we present LBP histogram Fourier (LBP-HF) features, and for dynamic-texture recognition, we present two rotation-invariant descriptors computed from the LBPs from three orthogonal planes (LBP-TOP) features in the spatiotemporal domain. LBP-HF is a novel rotation-invariant image descriptor computed from discrete Fourier transforms of LBP histograms. The approach can be also generalized to embed any uniform features into this framework, and combining the supplementary information, e.g., sign and magnitude components of the LBP, together can improve the description ability. Moreover, two variants of rotation-invariant descriptors are proposed to the LBP-TOP, which is an effective descriptor for dynamic-texture recognition, as shown by its recent success in different application problems, but it is not rotation invariant. In the experiments, it is shown that the LBP-HF and its extensions outperform noninvariant and earlier versions of the rotation-invariant LBP in the rotation-invariant texture classification. In experiments on two dynamic-texture databases with rotations or view variations, the proposed video features can effectively deal with rotation variations of dynamic textures (DTs). They also are robust with respect to changes in viewpoint, outperforming recent methods proposed for view-invariant recognition of DTs.
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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
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
Self-citationA 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
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A Statistical Approach to Texture Classification from Single Images
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Rotation invariant texture classification using LBP variance (LBPV) with global matching
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Dominant Local Binary Patterns for Texture Classification
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Outex - new framework for empirical evaluation of texture analysis algorithms
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Self-citationRotation Invariant Image Description with Local Binary Pattern Histogram Fourier Features
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Long, Y., Gong, Y., Xiao, Z., & Liu, Q. (2017) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2016.2645610 · Cited by 895
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
Self-citationPairwise Rotation Invariant Co-Occurrence Local Binary Pattern
Qi, X., Xiao, R., Li, C.-G., Qiao, Y., Guo, J., & Tang, X. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2316826 · Cited by 296
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Metadata
- Authors
- Zhao, G., Ahonen, T., Matas, J., & Pietikainen, M.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2012-04
- Volume
- 21
- Issue
- 4
- Pages
- 1465-1477
- ISSN
- 1057-7149 (ISSN-L)
- 1941-0042
- OpenAlex
- W2121452312
- Cited by
- 351
- References
- 25
- FWCI
- 19.32
- Type
- journal article
- Language
- en
- Open access
- closed
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- Zhao, G., Ahonen, T., Matas, J., & Pietikainen, M. (2012). Rotation-Invariant Image and Video Description With Local Binary Pattern Features. IEEE Transactions on Image Processing. https://doi.org/10.1109/tip.2011.2175739
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