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收稿：2017-07-17，
修回：2018-1-25，
纸质出版：2018-07-16
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目的
2
针对成对旋转不变的共生局部二值模式（PRICoLBP）算法对图像光照、旋转变化鲁棒性较差，且存在特征维度过大的问题，提出了一种可融合多种局部纹理结构信息的有效特征——增强成对旋转不变的共生扩展局部二值模式。
方法
2
首先，对图像各像素点的邻域像素点灰度值进行二值量化得到二值编码序列，并不断旋转二值序列得到以不同邻域点作为编码起始点对应的LBP值；然后，分别利用极大、极小LBP值对应的邻域起始编码点和中心像素点确定两个方向矢量，并沿这两个方向矢量在两个不同尺度图像上选取上下文共生点；其次，利用扩展局部二值模式（ELBP）算法的旋转不变均匀描述子来提取上下文共生点对的中心像素灰度级、邻域像素灰度级及径向灰度差异特征间的相关性信息；最后，用上下文共生点对的特征直方图训练卡方核支持向量机，检测纹理图像类别。
结果
2
通过对Brodatz、Outex（TC10、TC12）、Outex（TC14）、CUReT、KTH-TIPS和UIUC纹理库的分类实验，改进算法的识别率比原始的PRICoLBP算法识别率分别提高了0.32%、0.57%、5.62%、3.34%、2.1%、4.75%。
结论
2
利用像素点LBP特征极值对应的起始编码序列来选取上下共生点对，并用ELBP算法提取共生点对局部纹理信息，故本文方法能更好描述共生点对间的高阶曲率信息及更多局部纹理信息。在具光照、旋转变化的Outex、CUReT、KTH-TIPS纹理库图像分类实验中，所提方法比原始PRICoLBP算法取得了更高的识别率。实验结果表明，改进算法相比于原始算法能在较低的特征维度下对图像光照、旋转变化具有较好的鲁棒性。
Objective
2
As effective local texture descriptors
local binary pattern (LBP) and its variants are widely applied in various fields of image processing. As an LBP variant
the pairwise rotation invariant co-occurrence LBP (PRICoLBP) algorithm classifies texture better than other LBP variant algorithms do by extracting high-order curvature and contextual co-occurrence information between spatial context co-occurrence pixel points. However
PRICoLBP determines these points by using the pixel gray gradient vector
which changes with image rotation and describes the image texture by using the co-occurrence histogram between the original LBP and uniform LBP (LBP
u2
) features of the context co-occurrence pixels. Consequently
the PRICoLBP algorithm has poor robustness to variations in image illumination and rotation and has high computing feature dimensionality. An efficient texture feature named enhanced pairwise rotation-invariant co-occurrence extended LBP (ELBP)
which can fuse a variety of local texture structure information
is proposed in this work to address this problem.
Method
2
The binary coding sequence was obtained by performing binary quantization on the neighborhood pixel gray value of each pixel point. The LBP value corresponding to the different neighborhood points of each pixel
which was obtained by continuously rotating the binary coding sequence
was used as the initial point of coding. Then
two co-occurrence directional vectors with rotation invariance were determined using the central pixel point and the neighborhood initial points of coding corresponding to the maximum and minimum LBP values of each pixel
respectively. Two spatial context co-occurrence pixel points at different scales were selected along the two directional vectors on two grayscale images. Then
the correlation information among the central pixel gray level feature
the neighborhood pixel gray level feature
and the radial gray level difference feature of the spatial context co-occurrence points was extracted using the rotation-invariant uniform descriptor of the ELBP algorithm. The texture structure of a complex image was described by cascading the ELBP feature of each spatial context co-occurrence point. Finally
a chi-square kernel support vector machine
which was trained using texture feature histograms of the spatial context co-occurrence pixel pairs
was used to complete the detection of the image texture categories.
Result
2
Under the same experimental conditions
the classification recognition rate of the proposed method was improved by 0.32%
0.57%
5.62%
3.34%
2.1%
and 4.75% on the Brodatz
Outex (TC10
TC12)
Outex (TC14)
CUReT
KTH-TIPS
and UIUC texture databases
respectively
in comparison with the original PRICoLBP algorithm.
Conclusion
2
The initial encoding sequence corresponding to the maximum and minimum LBP feature values of each pixel point is used to select the spatial context co-occurrence pixel pairs
and the rotation-invariant uniform descriptor of the ELBP algorithm is adopted to capture the local texture structure information of the context co-occurrence pixel point pairs. Therefore
the proposed algorithm can describe the high-order curvature information and more local texture structure information between context co-occurrence pixel pairs better than the original PRICoLBP algorithm. In the image classification experiment on the Outex
CUReT
and KTH-TIPS image libraries
which have variations in texture illumination and rotation
the proposed algorithm not only exhibits a higher classification recognition rate than the original PRICoLBP algorithm but also has richer local texture feature patterns under reduced feature dimensionality. The experimental results show that the improved algorithm is more robust to variations in texture illumination and rotation than numerous state-of-the-art LBP variant algorithms under the same conditions. This algorithm can also be effectively applied in image classification with complex environment changes due to its high robustness and distinctivene.
Liu L, Kuang G Y. Overview of image textural feature extraction methods[J]. Journal of Image and Graphics, 2009, 14(4):622-635.
刘丽, 匡纲要.图像纹理特征提取方法综述[J].中国图象图形学报, 2009, 14(4):622-635. [DOI:10.11834/jig.20090409]
Vargas J F, Ferrer M A, Travieso C M, et al. Off-Line signature verification based on grey level information using texture features[J]. Pattern Recognition, 2011, 44(2):375-385.[DOI:10.1016/j.patcog.2010.07.028]
Huang A, Abugharbieh R, Tam R. A novel rotationally invariant region-based hidden Markov model for efficient 3-D image segmentation[J]. IEEE Transactions on Image Processing, 2010, 19(10):2737-2748.[DOI:10.1109/TIP.2010.2048965]
Choy S K, Tong C S. Statistical wavelet subband characterization based on generalized gamma density and its application in texture retrieval[J]. IEEE Transactions on Image Processing, 2010, 19(2):281-289.[DOI:10.1109/TIP.2009.2033400]
Galloway M M. Texture analysis using gray level run lengths[J]. Computer Graphics and Image Processing, 1975, 4(2):172-179.[DOI:10.1016/S0146-664X(75)80008-6]
Varma M, Zisserman A. A statistical approach to texture classification from single images[J]. International Journal of Computer Vision, 2005, 62(1-2):61-81.[DOI:10.1023/B:VISI.0000046589.39864.ee]
Ojala T, Pietikäinen M, Harwood D. A comparative study of texture measures with classification based on featured distributions[J]. Pattern Recognition, 1996, 29(1):51-59.[DOI:10.1016/0031-3203(95)00067-4]
Song K C, Yan Y H, Chen W H, et al. Research and Perspective on local binary pattern[J]. Acta Automatica Sinica, 2013, 39(6):730-734.
宋克臣, 颜云辉, 陈文辉, 等.局部二值模式方法研究与展望[J].自动化学报, 2013, 39(6):730-744. [DOI:10.3724/SP.J.1004.2013.00730]
Tan X Y, Triggs B. Enhanced local texture feature sets for face recognition under difficult lighting conditions[J]. IEEE Transactions on Image Processing, 2010, 19(6):1635-1650.[DOI:10.1109/TIP.2010.2042645]
Ren J F, Jiang X D, Yuan J S. Noise-resistant local binary pattern with an embedded error-correction mechanism[J]. IEEE Transactions on Image Processing, 2013, 22(10):4049-4060.[DOI:10.1109/TIP.2013.2268976]
Guo Z H, Zhang L, Zhang D. A completed modeling of local binary pattern operator for texture classification[J]. IEEE Transactions on Image Processing, 2010, 19(6):1657-1663.[DOI:10.1109/TIP.2010.2044957]
Zhao Y, Huang D S, Jia W. Completed local binary count for rotation invariant texture classification[J]. IEEE Transactions on Image Processing, 2012, 21(10):4492-4497.[DOI:10.1109/TIP.2012.2204271]
Heikkilä M, Pietikäinen M, Schmid C. Description of interest regions with local binary patterns[J]. Pattern Recognition, 2009, 42(3):425-436.[DOI:10.1016/j.patcog.2008.08.014]
Liao S, Law M W K, Chung A C S. Dominant local binary patterns for texture classification[J]. IEEE Transactions on Image Processing, 2009, 18(5):1107-1118.[DOI:10.1109/TIP.2009.2015682]
Liu L, Zhao L J, Long Y L, et al. Extended local binary patterns for texture classification[J]. Image and Vision Computing, 2012, 30(2):86-99.[DOI:10.1016/j.imavis.2012.01.001]
Qi X B, Xiao R, Li C G, et al. Pairwise rotation invariant co-occurrence local binary pattern[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(11):2199-2213.[DOI:10.1109/TPAMI.2014.2316826]
Ojala T, Pietikäinen M, Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(7):971-987.[DOI:10.1109/TPAMI.2002.1017623]
Liu L, Xie Y X, Wei Y M, et al. Survey of local binary pattern method[J]. Journal of Image and Graphics, 2014, 19(12):1696-1720.
刘丽, 谢毓湘, 魏迎梅, 等.局部二进制模式方法综述[J].中国图象图形学报, 2014, 19(12):1696-1720. [DOI:10.11834/jig.20141202]
Guo Z H, Zhang L, Zhang D. Rotation invariant texture classification using LBP variance (LBPV) with global matching[J]. Pattern Recognition, 2010, 43(3):706-719.[DOI:10.1016/j.patcog.2009.08.017]
Akata Z, Perronnin F, Harchaoui Z, et al. Good practice in large-scale learning for image classification[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(3):507-520.[DOI:10.1109/TPAMI.2013.146]
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