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Bi-dimensional Empirical Mode Decomposition and Nonconvex Penalty Minimization L q (q = 0.5) Regular Sparse Representation-based Classification for Image Recognition

  • Representation, Processing, Analysis, and Understanding of Images
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Abstract

This paper reports an innovative pattern recognition technique for fracture microstructure images based on Bi-dimensional empirical mode decomposition (BEMD) and nonconvex penalty minimization L q (q = 0.5) regular sparse representation-based classification (NPMLq-SRC) algorithm. The detailed procedures of this work can be divided into three steps, i.e., the preprocessing stage, the feature extraction stage and the image classification stage. We test and validate the proposed method through real data from metallic alloy fracture images. The case verification results show that our proposal can obtain a much higher recognition accuracy than the conventional Back Propagation Neural Networks (BPNN for short), the L1-norm minimization sparse representation-based classification (L1-SRC) and the BEMD combined with L1-norm minimization sparse representation-based classification (BEMD+L1-SRC) methods, respectively. Specifically, the proposed BEMD+NPMLq-SRC (q = 0.5) method outperforms the BEMD+L1-SRC method by 3.33% improvement of the average recognition accuracy, and outperforms L1-SRC method by 14.06% improvement of the average recognition accuracy, respectively.

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Authors and Affiliations

  1. College of Mechanical Engineering, Donghua University, Shanghai, 201620, P. R. China

    Qing Li, Xia Ji & S. Y. Liang

  2. George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, 30332-0405, USA

    S. Y. Liang

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  1. Qing Li
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  2. Xia Ji
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Correspondence to Qing Li.

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Qing Li received the Bachelor degree from the West Anhui University in 2012 and the Master degree from the Shanghai University of Science and Engineering in 2015. He is currently pursuing the PhD degree at the College of Mechanical Engineering, Donghua University, Shanghai. His research interests include dynamic signal/image processing, fault diagnosis for rotating/reciprocating machinery and compressed sensing, etc. He is also an invited peer-reviewer for many prestigious international journals such as Material and Design, Frontiers of Information Technology and Electronic Engineering and so on. In addition, he is the member of Institute of Electrical and Electronics Engineers (IEEE), the member of the American Society of Mechanical Engineers (ASME), and the member of Vibration Engineering Society of China (VESC), etc.

Xia Ji received her PhD degree from the Shanghai Jiaotong University in 2014. She has worked as a University Lecturer at the College of Mechanical Engineering, Donghua University, Shanghai. Her research interests include advanced cutting technology, machining thermodynamics, and computer vision, etc.

Steven Y. Liang holds a 1987 PhD in Mechanical Engineering from University of California at Berkeley, and is Professor (tenured) and Morris M. Bryan, Jr. Professor in Mechanical Engineering for Advanced Manufacturing Systems (permanently appointed) at Georgia Institute of Technology. He was the Institute’s founding Director of Precision Machining Research Consortium, Director of Manufacturing Education Program, and Associate Director of Manufacturing Research Center from 1996 to 2008. Prof. Liang’s technical interests lie in precision engineering, extreme manufacturing, and technology innovation. Prof. Liang served as President of the North American Manufacturing Research Institution (NAMRI) and Chair of the Manufacturing Engineering Division of The American Society of Mechanical Engineers (ASME). He is the recipient of many prestigious awards including the SME Robert B. Douglas Outstanding Young Manufacturing Engineer Award, Society of Automotive Engineers Ralph R. Teetor Educational Award, SME Blackall Machine Tool and Gage Award, and Outstanding Alumni Award of National Cheng Kung University. Prof. Liang has been elected Shanghai Thousand-Elite Expert and National Thousand-Elite Export of China. He is Fellow of both ASME and Society of Manufacturing Engineers International (SME).

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Li, Q., Ji, X. & Liang, S.Y. Bi-dimensional Empirical Mode Decomposition and Nonconvex Penalty Minimization L q (q = 0.5) Regular Sparse Representation-based Classification for Image Recognition. Pattern Recognit. Image Anal. 28, 59–70 (2018). https://doi.org/10.1134/S1054661818010133

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