Difference theoretic feature set for scale-, illumination- and rotation-invariant texture classification

Susan S.; Hanmandlu M.

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Difference theoretic feature set for scale-, illumination- and rotation-invariant texture classification

机译：用于比例，照明和旋转不变纹理分类的差异理论特征集

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Texture identification and classification under varying scale, rotation and illumination conditions is a challenging task in pattern recognition and grey level difference statistics have been extensively used for this purpose. This study presents a new set of features for scale-, rotation- and illumination-invariant texture classification derived from the correlated distributions of local and global grey level differences of intensities in the texture image. The authors analyse the terms in the correlation formula for determining the difference-based feature set that is invariant and unique for a texture class. A comprehensive evaluation is performed on a huge database of digitally created texture samples of varying scale, orientation and brightness. The one-nearest neighbour classifier is used in the authors' experiments and the results indicate high classification accuracy for the proposed feature vector under varying scale, rotation and brightness conditions. The proposed method is compared with the highly efficient rotation- and illumination-invariant local binary pattern (LBP) and LBP variance techniques and the scale- and rotation-invariant MRS4 technique and is found superior in performance with an additional advantage of reduced feature dimension.

机译：在规模，旋转和照明条件下进行纹理识别和分类是图案识别中的一项艰巨任务，灰度差异统计已广泛用于此目的。这项研究提出了一套新的缩放，旋转和照度不变纹理分类的功能，这些特征来自纹理图像中强度的局部和全局灰度差异的相关分布。作者分析了相关公式中的术语，以确定对于纹理类而言不变且唯一的基于差异的特征集。在庞大的数据库中进行了全面的评估，该数据库包含数字化创建的比例，方向和亮度不同的纹理样本。作者的实验中使用了一个最近邻分类器，结果表明在变化的比例，旋转和亮度条件下，所提出的特征向量具有很高的分类精度。将该方法与高效的旋转不变和照明不变局部二进制模式（LBP）和LBP方差技术以及缩放不变和旋转不变的MRS4技术进行了比较，发现其性能优越，并具有减少特征尺寸的优势。

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《Image Processing, IET》 |2013年第8期|725-732|共8页
作者
Susan S.; Hanmandlu M.;
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Electr. Eng. Dept., IIT Delhi, New Delhi, India|c|;

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1. Scale- and rotation-invariant texture description with improved local binary pattern features [J] . Reza Davarzani, Saeed Mozaffari, Khashayar Yaghmaie Signal processing . 2015,第juna期

机译：缩放和旋转不变的纹理描述，具有改进的局部二进制图案特征
2. Rotation-invariant features based on directional coding for texture classification [J] . Ouslimani Farida, Ouslimani Achour, Ameur Zohra Neural computing & applications . 2019,第10期

机译：基于纹理分类方向编码的旋转不变特征
3. Rotation-invariant Texture Classification by Spectral Transformation of Gabor Filter Features [J] . Gouchol Pok International Journal of Applied Engineering Research . 2018,第6aPta2期

机译：通过Gabor滤波器特征的光谱变换旋转 - 不变纹理分类
4. Rotation-invariant local radius index: A compact texture similarity feature for classification [C] . Yuanhao Zhai, Neuhoff David L. IEEE International Conference on Image Processing . 2014

机译：旋转不变局部半径索引：用于分类的紧凑纹理相似性特征
5. A novel approach in the detection of obstructive sleep apnea from electrocardiogram signals using neural network classification of textural features extracted from time-frequency plots. [D] . Al-Abed, Mohammad Ahmad. 2006

机译：一种使用从时频图提取的纹理特征的神经网络分类从心电图信号中检测阻塞性睡眠呼吸暂停的新方法。
6. A scale- and orientation-adaptive extension of Local Binary Patterns for texture classification [O] . Sebastian Hegenbart, Andreas Uhl -1

机译：局部二值模式的比例和方向自适应扩展用于纹理分类
7. Rotation-invariant texture classification using feature distributions [O] . M. Pietikäinen, T. Ojala, Z. Xu 2000

机译：使用特征分布的旋转不变纹理分类

Difference theoretic feature set for scale-, illumination- and rotation-invariant texture classification

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