Leaf Classification Utilizing a Convolutional Neural Network with a Structure of Single Connected Layer

Plant plays an important role in human life, so it is necessary to build an automatic system for recognizing plant. Leaf classification has become a research focus for twenty years. In this paper, we propose a single connected layer (SCL) structure adding into the convolutional neural network (CNN). We use this CNN model for plant leaf identification and report the promising results on ICL leaf database. Moreover, we propose some improvement on it to let it perform better. The result shows that our advanced SCL can effectively improve the accuracy of CNN.

[1]  Nikko Strom,et al.  Sparse connection and pruning in large dynamic artificial neural networks. , 1997 .

[2]  De-Shuang Huang,et al.  A Neural Root Finder of Polynomials Based on Root Moments , 2004, Neural Computation.

[3]  D.-S. Huang,et al.  Radial Basis Probabilistic Neural Networks: Model and Application , 1999, Int. J. Pattern Recognit. Artif. Intell..

[4]  Nitish Srivastava,et al.  Improving neural networks by preventing co-adaptation of feature detectors , 2012, ArXiv.

[5]  De-Shuang Huang,et al.  A Constructive Hybrid Structure Optimization Methodology for Radial Basis Probabilistic Neural Networks , 2008, IEEE Transactions on Neural Networks.

[6]  De-Shuang Huang,et al.  A Rayleigh-Ritz style method for large-scale discriminant analysis , 2014, Pattern Recognit..

[7]  Paul D. Gader,et al.  Morphological shared-weight networks with applications to automatic target recognition , 1997, IEEE Trans. Neural Networks.

[8]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[9]  Xiaofeng Wang,et al.  A Novel Density-Based Clustering Framework by Using Level Set Method , 2009, IEEE Transactions on Knowledge and Data Engineering.

[10]  De-Shuang Huang,et al.  Zeroing polynomials using modified constrained neural network approach , 2005, IEEE Transactions on Neural Networks.

[11]  Gaines E. Miles,et al.  Application of machine vision to shape analysis in leaf and plant identification , 1993 .

[12]  De-Shuang Huang,et al.  A constructive approach for finding arbitrary roots of polynomials by neural networks , 2004, IEEE Transactions on Neural Networks.

[13]  Isabelle Guyon,et al.  Design of a neural network character recognizer for a touch terminal , 1991, Pattern Recognit..

[14]  De-Shuang Huang,et al.  Efficient optimally regularized discriminant analysis , 2013, Neurocomputing.

[15]  Harris Drucker,et al.  Learning algorithms for classification: A comparison on handwritten digit recognition , 1995 .

[16]  Xiaofeng Wang,et al.  An efficient local Chan-Vese model for image segmentation , 2010, Pattern Recognit..

[17]  De-Shuang Huang,et al.  A General CPL-AdS Methodology for Fixing Dynamic Parameters in Dual Environments , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[18]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.