Plant Leaf Identification via a Growing Convolution Neural Network with Progressive Sample Learning

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• 作者：Zhong-Qiu Zhao (17) (18)
  Bao-Jian Xie (17)
  Yiu-ming Cheung (18) (20)
  Xindong Wu (17) (19)
  
  17. College of Computer Science and Information Engineering ; Hefei University of Technology ; Hefei ; China
  18. Department of Computer Science ; Hong Kong Baptist University ; Hong Kong SAR ; China
  20. United International College ; Beijing Normal University鈥揌ong Kong Baptist University ; Zhuhai ; China
  19. Department of Computer Science ; University of Vermont ; Burlington ; USA
  
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9004
• 期：1
• 页码：348-361
• 全文大小：2,297 KB
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• 作者单位：Computer Vision -- ACCV 2014
• 丛书名：978-3-319-16807-4
• 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

文摘

Plant identification is an important problem for ecologists, amateur botanists, educators, and so on. Leaf, which can be easily obtained, is usually one of the important factors of plants. In this paper, we propose a growing convolution neural network (GCNN) for plant leaf identification and report the promising results on the ImageCLEF2012 Plant Identification database. The GCNN owns a growing structure which starts training from a simple structure of a single convolution kernel and is gradually added new convolution neurons to. Simultaneously, the growing connection weights are modified until the squared-error achieves the desired result. Moreover, we propose a progressive learning method to determine the number of learning samples, which can further improve the recognition rate. Experiments and analyses show that our proposed GCNN outperforms other state-of-the-art algorithms such as the traditional CNN and the hand-crafted features with SVM classifiers.