Online learning method based on support vector machine for metallographic image segmentation

Mingchun Li, Dali Chen, Shixin Liu, Dinghao Guo

Abstract: The shape, size and distribution of the microstructure could certainly reveal mechanical properties. Therefore, it is important to segment the microstructure accurately. However, in view of the randomness in the metallographic detection period, a fixed segmentation algorithm with good performance in specific and known dataset may lead to wrong result when external condition changes. To solve the problem, we proposes an online learning pipeline to adjust the model adaptively by dynamic samples. First, a preprocessing method is deployed to smooth the uneven gray level. Next, local features and abstract features are extracted by a genetic algorithm-optimized boundary detector and a deep learning model, respectively. Then, online support vector machine is employed to update the model parameters under complex and changeable conditions in real time. Finally, a post-processing method is employed to get final result. A variety of experiments are presented to verify the effectiveness and the convergence of online algorithm. The experiment results indicate that the proposed pipeline can effectively extract local and abstract features, and the real-time updating model according to dynamic samples achieves state-of-the-art segmentation performance.