基于金字塔分解和扇形局部均值二值模式的鲁棒纹理分类方法

宋铁成; 罗林; 张刚; 罗忠涛; 张天骐

doi:10.11999/JEIT170884

基于金字塔分解和扇形局部均值二值模式的鲁棒纹理分类方法

doi: 10.11999/JEIT170884

基金项目:

国家自然科学基金(61702065, 61671095)，信号与信息处理重庆市市级重点实验室建设项目(CSTC2009CA2003)

计量
- 文章访问数: 1450
- HTML全文浏览量: 137
- PDF下载量: 165
- 被引次数: 0
出版历程
- 收稿日期: 2017-09-20
- 修回日期: 2018-02-01
- 刊出日期: 2018-06-19

Robust Texture Classification Method Based on Pyramid Decomposition and Sectored Local Mean Binary Pattern

Funds:

The National Natural Science Foundation of China (61702065, 61671095), The Project of Key Laboratory of Signal and Information Processing of Chongqing (CSTC2009 CA2003)

摘要

摘要: 针对传统局部二值模式(LBP)的特征鉴别力有限和噪声敏感性问题，该文提出一种基于金字塔分解和扇形局部均值二值模式的纹理特征提取方法。首先，将原始图像进行金字塔分解，得到对应于不同分解级别的低频和高频(差分)图像。为提取兼具鉴别力和稳健性的特征，进一步采用阈值化处理技术将高频图像转化为正、负高频图。然后，基于局部均值操作提出一种扇形局部均值二值模式(SLMBP)，用于计算各级分解图像的纹理特征码。最后，对纹理特征码进行跨频带的联合编码和跨级别的直方图加权，从而获得最终的纹理特征。在公开的3个纹理数据库(Outex, Brodatz和UIUC)上进行分类实验，结果表明该文所提方法能够有效地提高纹理图像在无噪声环境和含高斯噪声环境下的分类精度。
- 纹理分类 /
- 特征提取 /
- 局部二值模式 /
- 金字塔分解
Abstract: The traditional Local Binary Pattern (LBP) has limited feature discrimination and is sensitive to the noise. In order to alleviate these problems, this paper proposes a method to extract texture features based on pyramid decomposition and sectored local mean binary pattern. First, the pyramid decomposition is performed on the original image to obtain low-frequency and high-frequency (difference) images with different decomposition levels. To extract robust yet discriminative features, thresholding technique is further used to transform the high-frequency images into positive and negative high-frequency images. Then, based on local averaging operations, Sectored Local Mean Binary Pattern (SLMBP) is proposed and used to compute texture feature codes at different decomposition levels. Finally, the texture features are obtained by joint coding across frequency bands and by histogram weighting across decomposition levels. Experiments on three publicly available texture databases (Outex, Brodatz and UIUC) demonstrate that the proposed method can effectively improve the classification accuracy of texture images both in noise-free conditions and in the presence of different levels of Gaussian noise.
- Texture classification /
- Feature extraction /
- Local Binary Pattern (LBP) /
- Pyramid decomposition

HTML全文

参考文献(20)

WEN Dengwei, ZHANG Dongbo, TANG Hongzhong, et al. HEp-2 cell classification by fusing texture and shape features [J]. Journal of Electronics Information Technology, 2017, 39(7): 1599-1605. doi: 10.11999/JEIT161090.

文登伟, 张东波, 汤红忠, 等. 融合纹理与形状特征的HEp-2细胞分类[J]. 电子与信息学报, 2017, 39(7): 1599-1605. doi: 10. 11999/JEIT161090.

AL-SAHAF H, AL-SAHAF A, XUE B, et al. Automatically evolving rotation-invariant texture image descriptors by genetic programming[J]. IEEE Transactions on Evolutionary Computation, 2017, 21(1): 83-101. doi: 10.1109/TEVC.2016. 2577548.

JIA S, HU J, ZHU J S, et al. Three-dimensional local binary patterns for hyperspectral imagery classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(4): 2399-2413. doi: 10.1109/TGRS.2016.2642951.

SONG T C, LI H L, MENG F M, et al. Exploring space- frequency co-occurrences via local quantized patterns for texture representation[J]. Pattern Recognition, 2015, 48(8): 2621-2632. doi: 10.1016/j.patcog.2015.03.003.

WANG H, SANG M Y, HAN D K, et al. A feature descriptor based on the local patch clustering distribution for illumination-robust image matching[J]. Pattern Recognition Letters, 2017, 94: 46-54. doi: 10.1016/j.patrec.2017.05.010.

LIU L, FIEGUTH P W, GUO Y L, et al. Local binary features for texture classification: Taxonomy and experimental study[J]. Pattern Recognition, 2017, 62: 135-160. doi: 10.1016/j.patcog.2016.08.032.

OJALA T, PIETIKNEN M, and MENP 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.

GUO Z H and ZHANG L. 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.

PAN Z B, LI Z Y, FAN H C, et al. Feature based local binary pattern for rotation invariant texture classification[J]. Expert Systems with Applications, 2017, 88(12): 238-248. doi: 10.1016/j.eswa.2017.07.007.

SONG T C, LI H L, MENG F M, et al. Noise-robust texture description using local contrast patterns via global measures[J]. IEEE Signal Processing Letters, 2014, 21(1): 93-96. doi: 10.1109/LSP.2013.2293335.

REN J F, JIANG X D, and 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.

LIU L, LONG Y, FIEGUTH P W, et al. BRINT: Binary rotation invariant and noise tolerant texture classification[J]. IEEE Transactions on Image Processing, 2014, 23(7): 3071-3084. doi: 10.1109/TIP.2014.2325777.

ZHANG M and GUNTURK B K. Multiresolution bilateral filtering for image denoising[J]. IEEE Transactions on Image Processing, 2008, 17(12): 2324-2333. doi: 10.1109/TIP.2008. 2006658.

ZHAO Y, JIA W, HU R X, et al. Completed robust local binary pattern for texture classification[J]. Neurocomputing, 2013, 106(4): 68-76. doi: 10.1016/j.neucom.2012.10.017.

ZHAO Y, HUANG D S, and 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.

TAN X Y and 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.

HE J, JI H, and YANG X. Rotation invariant texture descriptor using local shearlet-based energy histograms[J]. IEEE Signal Processing Letters, 2013, 20(9): 905-908. doi: 10.1109/LSP.2013.2267730.

KHELLAH F M. Texture classification using dominant neighborhood structure[J]. IEEE Transactions on Image Processing, 2011, 20(11): 3270-3279. doi: 10.1109/TIP.2011. 2143422.

SHAKOOR M H and TAJERIPOUR F. Noise robust and rotation invariant entropy features for texture classification [J]. Multimedia Tools and Applications, 2017, 76(6): 8031-8066. doi: 10.1007/s11042-016-3455-6.

施引文献

资源附件(0)

访问统计