基于Gabor小波和LPP的浮选过程泡沫纹理特征提取及应用  

Extraction and Application of Froth Texture Feature Based on Gabor Wavelets and LPP in Flotation Process

作　　者：赵洪伟[1] 谢永芳[1] 曹斌芳[1] 蒋朝辉[1] 

机构地区：[1]中南大学信息科学与工程学院,长沙410083

出　　处：《上海交通大学学报》2014年第7期942-947,共6页Journal of Shanghai Jiaotong University

基　　金：国家自然科学基金资助项目(61134006);高等学校博士学科点专项基金博导类资助课题(20120162110076);高等学校博士学科点专项科研基金优先发展领域资助课题(20110162130011)

摘　　要：针对Gabor小波进行特征提取时易造成维数灾难和识别效率不高的问题,提出一种基于Gabor小波滤波和局部保持投影(LPP)降维算法相结合的泡沫纹理特征提取方法.首先,利用Gabor滤波器获得原始泡沫图像5个尺度和8个方向的高维特征描述向量;然后,利用LPP算法得到降维特征向量;最后,利用此降维特征向量通过反向传播(BP)神经网络进行不同工况下泡沫类别的识别,进而指导实际矿物浮选生产.实验结果表明,相对于传统的GLCM方法和Gabor小波纹理特征提取方法,该方法可有效降低泡沫纹理特征向量维数并具有更高的识别效率.Considering the problems of dimensional disaster and low recognition efficiency that appear when extracting texture feature using Gabor wavelets method only, a method based on both Gabor wavelets filter and LPP dimensionality reduction algorithm was proposed. First, the description of high dimensional feature vectors of five scales and eight orientations of the image were obtained by using Gabor filters.Next, lower-dimensional feature vectors were obtained by using LPP algorithm. Finally, the lower-dimen sional feature vectors were used to recognize different types of froth under different conditions using BP （back propagation） neural network to direct actual mineral manufacture. It is demonstrated by experimental results that this method has a less texture feature vector dimension and a higher recognition efficiency relative to the traditional methods based on GLCM and Gabor wavelets only when extracting texture feature.

关 键 词：浮选控制过程 纹理特征 GABOR小波 局部保持投影算法 反向传播神经网络识别 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]