多特征融合的金属断口图像分类

doi:10.16451/j.cnki.issn1003-6059.201805007

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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.

Key words： Multi-feature Fusion Metal Fracture Image Trace Transform Local Binary Pattern Dynamic Weighted Discrimination Power Analysis Multi-feature Fusion Metal Fracture Image Trace Transform Local Binary Pattern Dynamic Weighted Discrimination Power Analysis

Received: 18 January 2018

ZTFLH:

TP 391.4

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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	LU Yuming
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Cite this article:

LU Yuming,LI Ming,XING Dongdong等. Metal Fracture Image Classification Based on Adaptive Fusion of Multiple Features[J]. , 2018, 31(5): 453-461.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201805007 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2018/V31/I5/453

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