Metal Fracture Image Classification Based on Adaptive Fusion of Multiple Features
LI Ming1, XING Dongdong1, WANG Yuling2, LU Yuming1
1.Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition, Nanchang Hangkong University, Nanchang 330063 2.Jiangxi Engineering Laboratory on Radioactive Geoscience and Big Data Technology, East China University of Technology, Nanchang 330013
Abstract To enhance the discrimination ability of the extracted features of metal fracture images and improve the recognition rate of fracture images, a multi-feature fusion algorithm is presented by combining global and local texture features. Firstly, the global texture features of the image are extracted by Trace transform, and the local texture features are extracted by the local binary pattern. Then the dynamic weighted discrimination power analysis is employed for feature selection and adaptive weighted fusion. Finally, the classification are conducted by support vector machine. Experimental results on the image database of the metal fracture show that the proposed method produces a high recognition rate. The proposed algorithm also produces a high recognition rate and a strong generalization ability on other texture databases.
Corresponding Authors:
LI Ming, Ph.D., professor. His research interests include intelligent computing, image processing and pattern recognition.
About author:: WANG Yuling, Ph.D. candidate, asso-ciate professor. Her research interests include image processing and pattern recognition. XING Dongdong, master student. Her research interests include image processing and pattern recognition. LU Yuming, Ph.D., professor. Her research interests include optimization algorithm and image processing.
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2018, Vol. 31 
