Genetic Algorithm Based Feature Selection for Fracture Surface Images Classification

摘要：

Feature extraction and feature selection of fracture surface images provided by scanning electron microscopy (SEM) are two main challenges in classification of metal fracture surface images. In extracting features, the statistical characteristics of gray-level co-occurrence matrix and the fractal dimension of fracture surface images are computed as feature sets; and then, a genetic algorithm (GA) approach is presented to select a subset of features to discriminate different classes fracture surface images. A new fitness function based on minimum description length and maximum class separability is proposed to drive GA and it is compared with other feature selection method. Experimental results show that the GA driven by it selected a good subset of features to discriminate fracture surface images effectively.

关键词： geneti algorithm feature selection fracture surface image classification

作者: Li Ling LiMing LuYuMing Zhang YongLiang

作者单位: College of Automation Nanjing University of Aeronautics and Astronautics Nanjing, China Key Laboratory of Nondestructive Test Nanchang Hang Kong University Nanchang, China

会议类型: 国际会议

会议名称: 2009 International Conference on Measuring Technology and Mechatronics Automation(ICMTMA 2009)(2009年检测技术与机电自动化国际会议)

会议地点: 张家界

会议语种:英文

页码: 214-217

在线出版日期: 2009-04-11（万方平台首次上网日期，不代表论文的发表时间）