Palm Image Classification Using Multiple Kernel Sparse Representation Based Dictionary Learning

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• 关键词：Sparse coding ; Dictionary learning ; Multiple kernel function ; ELM classifier ; Sparse representation ; Palmprint classification
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9773
• 期：1
• 页码：128-138
• 全文大小：544 KB
• 参考文献：1.Thiagarajan, J.T., Ramamurthy, K.N., Spanias, A.: Multiple kernel sparse representations for supervised and unsupervised learning. IEEE Trans. Image Process. 23(7), 2905–2915 (2014)MathSciNet CrossRef
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• 作者单位：Pin-gang Su (16)
  Tao Liu (16)
  
  16. Department of Electrical Automation, College of Electronic Information Engineering, Suzhou Vocational University, Suzhou, 215104, Jiangsu, China
  
• 丛书名：Intelligent Computing Methodologies
• ISBN：978-3-319-42297-8
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9773

文摘

Sparse representation (SR) can effectively represent structure features of images and has been used in image processing field. A new palmprint image classification method by using multiple kernel sparse representation (MKSR) is proposed in this paper. Kernel sparse representation (KSR) behaves good robust and occlusion like as sparse representation (SR) methods. Especially, KSR behaves better classification property than common sparse representation methods and used widely in pattern recognition task. In KSR based classification methods, the selection of a kernel function and its parameters is very important. Usually, the kernel selected is not the most suitable and can not contain complete information. Therefore, MKSR methods are developed currently and used widely in image classification task. Here, multiple kernel functions select the weighted of Gauss kernel and polynomial kernel. In test, all palmprint images are selected from PolyU palmprint database. The palm classification task is implemented by the extreme learning machine (ELM) classifier. Compared with methods of SR and single kernel based SR, experimental results show that our method proposed has better calcification performance.