Classification method based on mineral flotation foam image

Abstract

The invention discloses a classification method based on a mineral flotation foam image. A real-time acquired foam image is classified in different known working conditions. The classification method comprises the following steps of: introducing a vocabulary in text classification into a flotation foam image, blocking the foam image acquired by an industrial camera and extracting the characteristic parameters, clustering the color and texture characteristic parameters of the extracted foam image by employing a K-mean clustering method, obtaining a plurality of clustering centers, and constructing a foam state vocabulary; describing the real-time foam image through a word bag method by utilizing the obtained foam state vocabulary, and forming a vector representation of the foam image; and finally, classifying the foam image through the similarity between measuring vectors by employing a vector space model. Because different types correspond to different working conditions, the flotation working condition recognition can be performed according to the classification result of the foam image; and therefore, the operation guide is given, the production is optimized, and the production efficiency is improved.

Description

[technical field] [0001] The invention belongs to the field of mineral flotation. In particular, a classification method for mineral flotation froth images. [Background technique] [0002] The mineral flotation production process is generally controlled by experienced workers observing the state of the flotation foam. The uncertainty of this operation is difficult to make the flotation process run in an optimal state. Using digital image processing technology to classify and interpret flotation froth images to obtain mineral flotation working condition information and optimize control is an effective method to improve economic benefits. At present, the research on the classification and identification of flotation foam images is mainly to describe the foam images by extracting the underlying characteristic parameters such as texture features, color features, and foam size distribution of flotation foam images, and then use neural networks or support vector machines, etc. C...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a classification method based on mineral flotation froth images in view of the problem of semantic gap and inaccurate classification in the image classification method based on the flotation foam bottom layer feature description

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