摘要
煤和矸石的图像分类是实现煤矸自动分选的关键环节。为提高煤矸分选模型的准确性和稳定性,提出了一种结合Relief、MRMR算法及SVM分类器构建的混合式特征选择及分类方法,提取煤矸图像的颜色及纹理共26个特征对其分类进行研究。在提取纹理时联合使用了LBP局部和GLCM全局特征,有助于提高分类的准确性。利用该特征选择方法选出最优特征子集后,用粒子群和支持向量机算法构建PSO-SVM最佳参数模型进行煤矸分类。结果显示,该方法能剔除较多冗余特征,提高煤矸分类的效率;在两个数据集上,该模型的平均分类准确率分别达到96.12%和94.17%,证明了方法的有效性和模型的稳定性。
The image classification of coal and gangue is the key to realize the automatic separation of coal and gangue images.To improve the accuracy and stability of coal gangue automatic separation model,a hybrid feature selection and classification method based on Relief,MRMR algorithm and SVM classifier is proposed in this paper.A total of 26 features of the color and texture of coal gangue image are used to study its classification.LBP local features and GLCM global features are combined to improve the accuracy of coal-gangue classification.After an optimal feature subset is selected,PSO and SVM are used to construct PSO-SVM optimal parameter model for coal gangue classification.The results show that this method can eliminate more redundant features and improve the efficiency of coal-gangue classification.The average classification accuracy of the model is 96.12%and 94.17%respectively on two different data sets,which proves the effectiveness of the method and the stability of the model.
作者
张释如
朱萌
ZHANG Shi-ru;ZHU Meng(College of Communication and Information Engineering,Xi'an University of Science and Technology,Xi'an 710054,China)
出处
《煤炭工程》
北大核心
2022年第4期139-144,共6页
Coal Engineering
基金
国家自然科学基金(61372175、51774234)
国家重点实验室基金会(2018SSFNKLSMT-13)
榆林市科技计划项目(CXY-2020-035)。
关键词
图像分类
特征选择
最大相关最小冗余算法
局部二值模式
煤和矸石
image classification
feature selection
max-relevance and min-redundancy(MRMR)algorithm
local binary patterns(LBP)
coal and gangue
