Hybrid Deep Learning for Plant Leaves Classification

Recently, deep learning is very popular, it has been applied into many applications, In this paper, a new neural network, hybrid deep learning is introduced, which included AutoEncoder(AE) and convolutional neural network (CNN). This neural network is applied for extracting the features of the plant leaves. In this paper, we proved that hybrid deep learning can extract better features for classification task. We apply the hybrid deep learning to extract features of leaf pictures, and then we classify leaves using those features with SVM, the result suggests that this method is not only better than pure SVM, but also better than pure AE and pure CNN.

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

[2]  Sergios Theodoridis,et al.  A geometric approach to Support Vector Machine (SVM) classification , 2006, IEEE Transactions on Neural Networks.

[3]  X. Chen,et al.  SVM-Prot: web-based support vector machine software for functional classification of a protein from its primary sequence , 2003, Nucleic Acids Res..

[4]  Yoshua Bengio,et al.  An empirical evaluation of deep architectures on problems with many factors of variation , 2007, ICML '07.

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

[6]  Pascal Vincent,et al.  Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Honglak Lee,et al.  Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations , 2009, ICML '09.

[8]  De-Shuang Huang,et al.  Locally linear discriminant embedding: An efficient method for face recognition , 2008, Pattern Recognit..

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

[10]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[11]  Luca Maria Gambardella,et al.  Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence Flexible, High Performance Convolutional Neural Networks for Image Classification , 2022 .

[12]  Larry S. Davis,et al.  Polarograms: A new tool for image texture analysis , 1979, Pattern Recognit..

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

[14]  Barbara Caputo,et al.  Recognizing human actions: a local SVM approach , 2004, ICPR 2004.

[15]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[16]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.