针对现有煤岩识别方法在训练样本不充足情况下的识别效果普遍不太理想这一情况,提出了一种基于完备局部二值模式(CLBP)和支持向量诱导字典学习的煤岩识别方法。该方法分4大步完成:(1)提取煤岩图像的多尺度CLBP特征向量;(2)对训练样本的CLBP特征向量进行支持向量诱导字典学习,得到一组煤岩表征字典、煤岩类别权向量和偏移量;(3)计算测试样本在煤岩表征字典上的表示即编码向量;(4)采用判别函数完成测试样本编码向量的类别判定。结果表明:与现有其他常用方法相比,所提出方法有着更高的正确识别率,特别是在训练样本不充分的随机抽样实验条件下,其正确识别率仍然很高;耗时的字典学习并没有影响到所提出方法的实时性;所提出方法占用的存储量不受训练样本数量的制约,这在一定程度上为将来硬件实现带来了便利。

In view of the fact that few existing coal-rock recognition methods achieve a satisfactory performance when training samples are insufficient,an effective coal-rock recognition approach based on completed local binary patterns (CLBP) and support vector guided dictionary learning was proposed. It includes four steps as follows: Firstly, the multi-scale CLBP-based feature vectors of the coal and rock images were obtained. Secondly,support vector guided dictionary learning was conducted on the feature vectors of training samples. After that,one dictionary for coal-rock characterization,several weight vectors and biases for coal-rock identification were earned. Thirdly,the representation of the multi-scale CLBP feature vector of the test sample with respect to the dictionary (i. e. ,the code vector) was ac- quired. Finally,the categorization regarding the code vector was completed by using discriminant function. Experimen- tal results demonstrate that compared with other existing approaches,the proposed one yields a higher correct recogni- tion rate. Furthermore,it has a very high recognition accuracy even in sample-insufficient random sampling experi- ments. The time-consuming dictionary learning involved in the proposed approach scarcely hampers its realtimeness. The storage requirement for this approach does not depend on the number of training samples,which facilitates its hardware implementation in the future to some extent.