School of Life Sciences and Technology, Xidian University, Xi’an 710071, China.
IEEE Trans Image Process. 2013 Jan;22(1):31-42. doi: 10.1109/TIP.2012.2214045. Epub 2012 Aug 17.
Local energy pattern, a statistical histogram-based representation, is proposed for texture classification. First, we use normalized local-oriented energies to generate local feature vectors, which describe the local structures distinctively and are less sensitive to imaging conditions. Then, each local feature vector is quantized by self-adaptive quantization thresholds determined in the learning stage using histogram specification, and the quantized local feature vector is transformed to a number by N-nary coding, which helps to preserve more structure information during vector quantization. Finally, the frequency histogram is used as the representation feature. The performance is benchmarked by material categorization on KTH-TIPS and KTH-TIPS2-a databases. Our method is compared with typical statistical approaches, such as basic image features, local binary pattern (LBP), local ternary pattern, completed LBP, Weber local descriptor, and VZ algorithms (VZ-MR8 and VZ-Joint). The results show that our method is superior to other methods on the KTH-TIPS2-a database, and achieving competitive performance on the KTH-TIPS database. Furthermore, we extend the representation from static image to dynamic texture, and achieve favorable recognition results on the University of California at Los Angeles (UCLA) dynamic texture database.
局部能量模式是一种基于统计直方图的表示方法，用于纹理分类。首先，我们使用归一化的局部方向能量生成局部特征向量，这些特征向量能够突出地描述局部结构，并且对成像条件的敏感性较低。然后，每个局部特征向量都通过自适应量化阈值进行量化，这些阈值是在学习阶段使用直方图规范确定的。量化后的局部特征向量通过 N 元编码转换为数字，这有助于在向量量化过程中保留更多的结构信息。最后，使用频率直方图作为表示特征。在 KTH-TIPS 和 KTH-TIPS2-a 数据库上进行材料分类的基准测试来评估性能。我们的方法与典型的统计方法（如基本图像特征、局部二值模式（LBP）、局部三元模式、完整 LBP、Weber 局部描述符和 VZ 算法（VZ-MR8 和 VZ-Joint））进行了比较。结果表明，我们的方法在 KTH-TIPS2-a 数据库上优于其他方法，在 KTH-TIPS 数据库上也取得了有竞争力的性能。此外，我们将表示从静态图像扩展到动态纹理，并在加利福尼亚大学洛杉矶分校（UCLA）动态纹理数据库上获得了良好的识别结果。
