Pattern Recognition
Pattern Recognition
复制标题
DOI：
10.1049/iet-bmt.2018.5117
复制
发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Sheng He;Lambert Schomaker
中科院分区：
文献类型：
--
作者：
Sheng He;Lambert Schomaker
特征工程在作者识别中起着非常重要的作用，在文献中得到了广泛的研究。以往的研究表明，两种性质的联合特征分布可以提高性能。联合特征分布使特征关系显式化，而不是要求训练好的分类器拾取数据中存在的非线性关系。在本文中，我们提出了两个新的和曲率无关的特征：局部二进制模式的运行长度（LBPruns）和线云分布（COLD）特征用于作家识别。LBP prun是传统的游程长度和局部二值模式（LBP）方法的联合分布，它计算二值化图像和灰度图像上的局部二值模式的游程长度。COLD特征是手写文档中书写轮廓线段的方向与长度关系的联合分布。我们提出的lbprun和COLD是基于纹理的无曲率特征，它们捕获手写文本的线条信息而不是曲率信息。LBPruns和COLD特征的结合对CERUG数据集（包含大量不规则曲率笔画的手写文档）提供了显著的改进。在另外两个广泛使用的数据集（Firemaker和IAM）上评估所提出的特征的结果显示出有希望的结果。
Feature engineering takes a very important role in writer identi fi cation which has been widely studied in the literature. Previous works have shown that the joint feature distribution of two properties can improve the performance. The joint feature distribution makes feature relationships explicit instead of roping that a trained classi fi er picks up a non-linear relation present in the data. In this paper, we propose two novel and curvature-free features: run-lengths of local binary pattern (LBPruns) and cloud of line distribution (COLD) features for writer identi fi cation. The LBPruns is the joint distribution of the traditional run-length and local binary pattern (LBP) methods, which computes the run-lengths of local binary patterns on both binarized and gray scale images. The COLD feature is the joint distribution of the relation between orientation and length of line segments obtained from writing contours in handwritten documents. Our proposed LBPruns and COLD are textural-based curvature-free features and capture the line information of handwritten texts instead of the curvature information. The combination of the LBPruns and COLD features provides a signi fi cant improvement on the CERUG data set, handwritten documents on which contain a large number of irregular-curvature strokes. The results of proposed features evaluated on other two widely used data sets (Firemaker and IAM) demonstrate promising results.