基于最大池化稀疏编码的煤岩识别方法

针对现今煤岩图像识别方法的缺乏与不足,为了挖掘新的煤岩图像识别方法以及更好地处理高维煤岩图像数据,提出了基于最大池化稀疏编码的煤岩识别方法.本方法在提取煤岩图像特征时加入了池化操作,在分类识别时采用了集成分类器,即多个弱分类器组成一个强分类器.实验结果表明:最大池化稀疏编码的特征提取方式能简单有效表达煤岩图像的纹理特征,大大增强煤岩图像的可区分性,获得较高的识别率,并且具有良好的识别稳定性.研究结果可为煤岩界面的自动识别提供新的思路和方法.

A coal-rock recognition method based on max-pooling sparse coding

Because of the lack of coal-rock methods, a novel coal-rock recognition method was proposed based on max-pooling sparse coding in order to explore new coal-rock image recognition methods and efficiently handle high-dimensional coal-rock image data.This method adds the pooling operation when extracting coal-rock image features and adopts the integrated classifier, which consists of multiple weak classifiers when classifying coal-rock images.The experimental results show that this feature-extraction method based on max-pooling sparse coding can simply and effectively express the characteristic information of coal-rock images, greatly enhance the distinguishability of coal-rock images, and achieve a high recognition rate.This method also has good recognition stability.The results obtained herein could provide a new idea and method for automatic coal-rock interface recognition.