Bubble-Forming Regime Identification Based on Image Textural Features and the MCWA Feature Selection Method

Knowledge of the bubble-forming regime is important for studying the mechanism of gas-liquid two-phase flow. The process of bubble formation through an underwater nozzle at the bottom of a rectangular tank was observed using a high-speed camera. Five bubble-forming regimes were classified by considering both the bubble interaction and the periodic behavior. To automatically identify the bubble-forming regimes, 22 image textural features were extracted based on the grey-level co-occurrence matrix and grey-level gradient co-occurrence matrix. A feature selection method, the mathematical correlation weight algorithm (MCWA), was proposed to select the optimal textural feature subset for bubble-forming regime recognition. Experimental results demonstrated that the identification rates based on the feature subset selected by MCWA were 99.81% and 99.24% for support vector machine and artificial neural network, respectively, which were higher than those by the between-class distance method and the Fisher algorithm. The MCWA proposed in this paper is an effective feature selection method and can be widely used in pattern recognition.