Texture Feature Extraction And Classification Based On The VSM With An Application In Aluminum Froth Floatation

In recent years, digital image technology has been widely researched and applied in the process of mineral flotation. Usually, the flotation condition can be distinguished by studying the variation of the foam surface characteristic parameters. However, the environment of the flotation scene is always very bad, result in the quality of the froth image is low and affecting the accuracy of flotation bubble image feature extraction. It will ultimately affect the precision of the flotation foam figure classification recognition rate. In order to reduce the bad effects of industrial field on image quality, improve the accuracy of froth image classification, this paper proposed a method of froth image texture feature extraction and classification based on vector space model (VSM).(1) In this paper, the research status of image texture extraction and classification methods are summarized at first. In view of the bubble image feature extraction is affected by image quality, method of texture feature extraction based on VSM is proposed. This method blocks image of database reasonably, and extracts the color co-occurrence matrix (CCM) characteristics of each block. The image’s CCM texture data table is got by using the fuzzy c-means clustering on all CCM feature vectors and weighted by using the relative TF-MI (Term frequency-Mutual information) weighting factor. Then, the CCM texture vector of each block is contrasted with the weighted data table and state of each block is marked. At last, the frequency of each state is censused and the image is represented by CCM texture vector called CCM texture feature word bag representation.(2) In order to use the word bag of CCM texture to reflect the picture’s feature type, this paper applied BP and LVQ neural net model to classify the word bag data. The CCM texture word bag vector set and the set of image are used to study the problem of image classification. Because of BP network stability is affected by the hidden layer nodes, LVQ network’s precision is low and raining time is too long, this paper designed a classification model named BP-LVQ reliable net combination model. This model used three-layer network, chose8BP networks as bottom layer to overcome the low precision of LVQ network. The8BP networks are divided into4groups and each group used different number of hidden layer nodes. Then, the mean value of each group’s result is used as input to the middle layer and the middle lay includes4same structure LVQ networks. It can synthesize the output results of different BP networks in bottom layer and the affection of hidden layer nodes can be solved by this model too. At last, the4outputs of LVQ network in top layer according to reliable linear combination algorithm and the top LVQ network output the final classification result.(3)Use the method texture feature extraction and classification based on the VSM application in aluminum froth floatation. The result show that the flotation froth feature extraction and classification method can improve the classification accuracy, and can provide reference for intelligent control of flotation process.