LTGH: A Dynamic Texture Feature for Working Condition Recognition in the Froth Flotation

• Published in: IEEE transactions on instrumentation and measurement Vol. 70; pp. 1 - 10
• Main Authors: Luo, Jin, Tang, Zhaohui, Zhang, Hu, Fan, Ying, Xie, Yongfang
• Format: Journal Article
• Language: English
• Published: New York IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Data mining; Dynamic texture; Employee welfare; Feature extraction; Feature maps; Feature recognition; Flotation; froth flotation; gray-level co-occurrence matrix (GLCM); Histograms; Image recognition; Lighting; local binary pattern (LBP); Manufacturing processes; Object recognition; Orthogonality; Rotation; Texture recognition; three orthogonal planes (TOP); working condition recognition
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2021.3065417

Texture feature of the froth image is widely used in the working condition recognition of froth flotation. However, due to the complexity of the froth image, the current texture features vary greatly and are difficult to identify the work condition accurately. Therefore, we propose a dynamic texture feature named LBP on the TOP and GLCM Histograms (LTGH) which integrates the local binary patterns (LBPs) and gray-level co-occurrence matrix (GLCM) histograms on the three orthogonal planes (TOP). First, we use the rotation invariant LBPs to enhance rotation invariance and illumination robustness. Then, we implement the TOP on the enhanced texture feature map to generate the multiple dimensional enhanced feature maps. After that, we calculate the GLCM and supplementary features (SFs) on the multiple dimensional enhanced feature map. Finally, we integrate the histogram of the GLCM and SFs to discriminate the texture feature. The LTGH feature considers the froth structures both in the macrolevel and microlevel and captures the temporal information between the froth images. Experiments have demonstrated the effectiveness and stability of the proposed texture feature for work condition recognition in froth flotation. Compared with other traditional texture features, the accuracy of the LTGH feature has been increased by at least 7.76%.