An effective scheme for image texture classification based on binary local structure pattern
An effective scheme for image texture classification based on binary local structure pattern
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DOI：
10.1007/s00371-013-0887-0
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发表时间：
2014-11
期刊：
影响因子：
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通讯作者：
Nishant Shrivastava;V. Tyagi
中科院分区：
文献类型：
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作者：
Nishant Shrivastava;V. Tyagi
局部二值模式(LBP)特征在纹理图像分类和检索领域的有效性得到了很好的证明。本文提出了一种更有效的完整的LBP建模方法。传统的LBP有一个缺点，即有时它可能用相同的LBP代码表示不同的结构模式。此外，LBP还缺乏全球信息，对噪音很敏感。文中提出了用阈值作为中心像素值和平均局部差值之和的二值模式。提出的局部结构模式(LSP)能够更准确地对不同的纹理结构进行分类，因为它们同时利用了局部和全局信息。LSP可以与简单的LBP和中心像素模式相结合，得到完整的局部结构模式(CLSP)，从而实现更高的分类精度。为了使CLSP对噪声不敏感，还提出了一种鲁棒局部结构模式(RLSP)。该方案在三个具有代表性的纹理数据库上进行了测试。OUTEX、Curet和UIUC。实验结果表明，该方法在获得较高分类精度的同时，对噪声具有较强的鲁棒性。
Effectiveness of local binary pattern (LBP) features is well proven in the field of texture image classification and retrieval. This paper presents a more effective completed modeling of the LBP. The traditional LBP has a shortcoming that sometimes it may represent different structural patterns with same LBP code. In addition, LBP also lacks global information and is sensitive to noise. In this paper, the binary patterns generated using threshold as a summation of center pixel value and average local differences are proposed. The proposed local structure patterns (LSP) can more accurately classify different textural structures as they utilize both local and global information. The LSP can be combined with a simple LBP and center pixel pattern to give a completed local structure pattern (CLSP) to achieve higher classification accuracy. In order to make CLSP insensitive to noise, a robust local structure pattern (RLSP) is also proposed. The proposed scheme is tested over three representative texture databases viz. Outex, Curet, and UIUC. The experimental results indicate that the proposed method can achieve higher classification accuracy while being more robust to noise.