Abstract: Aiming at the problem that the texture structure of the flotation foam images in different states is similar and the color difference is not obvious, a method based on hue, saturation, value(HSV) color space for completed local binary pattern(CLBP) texture extraction is proposed.Firstly, the dual-domain image denoising is used to filter the image noise while preserving the texture details, and then converted to HSV images, and the three-scale CLBP texture features are extracted on the H, S and V color components respectively.The extracted texture features are normalized and linearly arranged to establish a high-dimensional texture classification model.Finally, through the one-versus-one support vector machine classifier, the four-class bubble state sample set is subjected to texture extraction and classification training and testing.The results show that the method has higher classification accuracy for different flotation foam states, and is superior to other texture extraction methods, which is suitable for the identification of flotation foam states.