为了进一步加强金属断口图像特征的鉴别能力,提高断口图像的识别率,提出基于全局与局部纹理特征的多特征融合算法.首先利用Trace变换提取图像全局纹理特征,局部二值模式提取图像局部纹理特征.然后采用动态加权鉴别能量分析对2种特征进行优选和自适应加权融合.最后采用支持向量机进行分类识别.在金属断口图像库上实验表明,文中方法识别率较高,在其它的纹理数据库上具有较好的泛化能力.%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.
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