## 1 Introduction  
The aim of the mineral flotation process is to separate valuable minerals from useless materials or other minerals so as to obtain upgraded minerals. The concentrate grade is measured by calculating the percentage of the recovered useful element mass in the total concentrate mass. As a key performance indicator to evaluate the flotation effect in metallurgical enterprises, the concentrate grade directly reflects the quality of flotation working conditions. When the grade of the concentrate is too low, the working conditions of the flotation process will be in an abnormal state. The operator should adjust the flotation operating variables such as the inlet air flow and pulp level, related to the flotation working conditions in order to improve the concentrate grade. The better the flotation working conditions, the higher the concentrate grade. Thus, the concentrate grade reflects the flotation working conditions. In the process of mineral flotation, the process working conditions are judged mainly according to the color, size and other features of the flotation froth, and operating parameterssuch as the air blowing amount are adjusted to ensure that the concentrate grade reaches the standard. Due to strong subjective and labor-intensive manual control, the efficiency of production is low, and the grade of the concentrate cannot be guaranteed. Therefore, researchers have studied the features of flotation froth images and found that the image features of mineral flotation froth are closely related to the operating parameters, working conditions and concentrate grade in the flotation process. Researchers continue to obtain shallow features, such as the color, texture and size of mineral flotation froth images, through image processing technology to identify the working conditions of the flotation process [1; 2; 3; 4; 5] and realize the optimal control of the flotation process. The extracted flotation froth image features, however, contain an excessive amount of redundant and noisy information, resulting in the low accuracy of working condition recognition and affecting flotation production efficiency. Therefore, it is of great significance to achieve the optimal control of the flotation process and improve the mineral flotation concentrate grade by studying a more effective mineral flotation process working condition recognition method. The key to realizing the optimal control of the flotation process is the accurate extraction of flotation froth image features. Therefore, researchers are committed to extracting the features of flotation froth images and identifying the working conditions of the flotation process on this basis [7; 8; 9; 10]. Such features include the following. The color of the froth can reflect the types of particles carried by the froth and the quality of the flotation working conditions during the flotation process. In the past, many researchers have described color information mainly by calculating statistics such as the mean and standard deviation of each component of the color space, such as RGB, HSV and HIS [11; 12; 13]. However, the extraction of color information is susceptible to lighting. The texture of the froth, which is primarily represented by calculating the arrangement rules and local patterns of the local gray value of the image, partially reflects the quality of the flotation process. In the past, domestic and foreign researchers extracted the texture features of flotation froth mainly by using the gray-level co-occurrence matrix (GLCM) [14], wavelet transform [15] and texture spectrum [16]. The GLCM describes the texture features through the calculation of second-order statistics in all directions, leading to the problem that high-dimensional matrices are computationally intensive, and it is difficult to completely describe the froth texture features through single-direction statistics.