MRMR和SSGMM联合分类模型的煤泥浮选系统药况图像识别

为解决煤泥浮选过程依靠工人肉眼识别泡沫特征来调节药剂用量,造成药剂浪费,产品质量不合格的问题,提出一种MRMR和SSGMM联合分类模型的药况图像识别方法.针对泡沫图像的形态、纹理、颜色特征与泡沫类别具有不同程度的相关性.将精煤灰分作为泡沫的类别信息,利用最大相关最小冗余(MRMR)算法筛选最优特征;针对传统的高斯混合模...

Recognition of reagent dosage condition image for coal flotation system based on joint classification model of MRMR and SSGMM

In order to solve the problem that the coal flotation process depends on the naked eyes of the workers toidentify the froth features to adjust the dosage of reagent which results in the waste of reagents and the unqualified product,an recognition method of reagent dosage condition image based on joint classification model of MRMR and SSGMMis proposed. With respect to the different degrees of correlations between the morphology, texture and color features ofthe froth image and the froth classification, the ash content of clean coal is taken as the classification information, theoptimal froth image features are screened out by maximal-relevance-minimal-redundancy (MRMR) algorithm; aiming atthe problem that the results need to be judged artificially to realize the classification when the clustering of traditionalGaussian mixture model (GMM), it is improved by introducing a small number of froth image feature samples underthe condition of known reagent dosage, a semi-supervised Gaussian mixture model (SSGMM) cluster is constructed. Theoptimal multi-dimensional froth image features with a small amount of prior label information are integrated into theSSGMM clustering model, the clustering is guided by labeling samples, and their label information is mapped to theclustering result, so that the automatic classification is realized. The experimental results show that the accuracy of frothrecognition has been improved by this kind of joint classification model, and the key technical support has been providedfor the accurate control of the dosage of reagent and the quality of clean coal products.