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.
黎明, 邢冬冬, 汪宇玲, 鲁宇明. 多特征融合的金属断口图像分类[J]. 模式识别与人工智能, 2018, 31(5): 453-461.
LI Ming, XING Dongdong, WANG Yuling, LU Yuming. Metal Fracture Image Classification Based on Adaptive Fusion of Multiple Features. , 2018, 31(5): 453-461.
[1] 钟群鹏,赵子华,张 峥.断口学的发展及微观断裂机理研究.机械强度, 2005, 27(3): 358-370. (ZHONG Q P, ZHAO Z H, ZHANG Z. Development of Fractography and Research of Fracture Micromechanism. Journal of Mechanical Strength, 2005, 27(3): 358-370.) [2] 韩太坤,李志农.基于灰度共生矩阵与局部线性嵌入的金属断口图像识别方法研究.机械强度, 2014, 36(1): 129-133. (HAN T K, LI Z N. Recognition Method of Metal Fracture Image Based on Gray Level Co-occurrence Matrix and Locally Linear Embedding. Journal of Mechanical Strength, 2014, 36(1): 129-133.) [3] 余丽萍,黎 明,杨小芹,等.基于灰度共生矩阵的断口图像识别.计算机仿真, 2010, 27(4): 224-227. (YU L P, LI M, YANG X Q, et al. Recognition of Fracture Image Based on Gray Level Co-occurrence Matrix. Computer Simulation, 2010, 27(4): 224-227.) [4] 李 凌,黎 明,鲁宇明.基于模糊灰度共生矩阵与隐马尔可夫模型的断口图像识别.中国图象图形学报, 2010, 15(9): 1370-1375. (LI L, LI M, LU Y M. Recognition of Fracture Surface Images Based on Fuzzy Gray Level Co-occurrence Matrix and Hidden Markov Model. Journal of Image and Graphics, 2010, 15(9): 1370-1375.) [5] 李志农,孙 熠,闫敬文,等.基于Grouplet-RVM的金属断口图像识别方法研究.仪器仪表学报, 2014, 35(6): 1347-1353. (LI Z N, SUN Y, YAN J W, et al. Study on the Recognition Method of Metal Fracture Images Based on Grouplet-RVM. Chinese Journal of Scientific Instrument, 2014, 35(6): 1347-1353.) [6] 李志农,陈 康,闫敬文,等.基于Grouplet-KPCA金属断口图像识别方法研究.机械强度, 2016, 38(1): 1-5. (LI Z N, CHEN K, YAN J W, et al. Recognition Method of Metal Fracture Images Based on Grouplet Transform and Kernel Principle Component Analysis. Journal of Mechanical Strength, 2016, 38(1):1-5.) [7] 刘 帅,李士进,冯 钧.多特征融合的遥感图像分类.数据采集与处理, 2014, 29(1): 108-115. (LIU S, LI S J, FENG J. Remote Sensing Image Classification Based on Adaptive Fusion of Multiple Features. Journal of Data Acquisition and Processing, 2014, 29(1): 108-115.) [8] 刘帅师,田彦涛,万 川.基于Gabor多方向特征融合与分块直方图的人脸表情识别方法.自动化学报, 2011, 37(12): 1455-1463. (LIU S S, TIAN Y T, WAN C. Facial Expression Recognition Method Based on Gabor Multi-orientation Features Fusion and Block Histogram. Acta Automatica Sinica, 2011, 37(12): 1455-1463.) [9] 赵高鹏,沈玉鹏,王建宇.基于核循环结构的自适应特征融合目标跟踪.光学学报, 2017, 37(8): 201-210. (ZHAO G P, SHEN Y P, WANG J Y. Adaptive Feature Fusion Object Tracking Based on Circulant Structure with Kernel. Acta Optica Sinica, 2017, 37(8): 201-210.) [10] 林志洁,丰明坤.深度视觉特征与策略互补融合的图像质量评价.模式识别与人工智能, 2017, 30(8): 682-691. (LIN Z J, FENG M K. Image Quality Assessment Based on Complementary Pooling of Deeply Visual Feature and Strategy. Pattern Recognition and Artificial Intelligence, 2017, 30(8): 682-691.) [11] 罗会兰,单顺勇,孔繁胜.融合多特征的加权分布跟踪.模式识别与人工智能, 2016, 29(2): 131-142. (LUO H L, SHAN S Y, KONG F S. Object Tracking Model Based on Fusing Multiple Weighted Distribution Field Features. Pattern Recognition and Artificial Intelligence, 2016, 29(2): 131-142.) [12] KADYROV A, PETROU M. The Trace Transform and Its Applications. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(8): 811-828. [13] KADYROV A, TALEPBOUR A, PETROU M. Texture Classification with Thousands of Features//Proc of the British Machine Vision Conference. Berlin, Germany: Springer, 2003: 656-665. [14] NASRUDIN M F, PETROU M, KOTOULAS L. Jawi Character Recognition Using the Trace Transform//Proc of the 7th International Conference on Computer Graphics, Imaging and Visualization. Washington, USA: IEEE, 2010: 151-156. [15] GOUDELIS G, KARPOUZIS K, KOLLIAS S. Exploring Trace Transform for Robust Human Action Recognition. Pattern Recognition, 2013, 46(12): 3238-3248. [16] FRAS-VELZQUEZ A, ORTIZ C, PIURICA A, et al. Object Identification by Using Orthonormal Circus Functions from the Trace Transform//Proc of the 19th IEEE International Conference on Image Processing. Washington, USA: IEEE, 2013: 2153-2156. [17] PETROU M, WANG F. A Tutorial on the Practical Implementation of the Trace Transform//MIRMEHDI M, XIE X H, SURI J, eds. Handbook of Texture Analysis. Singapore, Singapore: World Scientific, 2008: 313-346. [18] 宋克臣,颜云辉,陈文辉,等.局部二值模式方法研究与展望.自动化学报, 2013, 39(6): 730-744. (SONG K C, YAN Y H, CHEN W H, et al. Research and Perspective on Local Binary Pattern. Acta Automatica Sinica, 2013, 39(6): 730-744.) [19] DABBAGHCHIAN S, GHAEMMAGHAMI M P, AGHAGOLZADEH A. Feature Extraction Using Discrete Cosine Transform and Discrimination Power Analysis with a Face Recognition Technology. Pattern Recognition, 2010, 43(4): 1431-1440. [20] 徐卓飞,张海燕,刘 凯,等.基于Radon-经验模式分析的纹理分类.中国图象图形学报, 2015, 20(8): 1091-1101. (XU Z F, ZHANG H Y, LIU K, et al. Texture Classification Based on Radon-Empirical Mode Decomposition Analysis. Journal of Image and Graphics, 2015, 20(8): 1091-1101.)
2018, Vol. 31 
