Flower image classification based on generative adversarial network and transfer learning

Aiming at the problem that the classification accuracy of the traditional flower classification method is low and the deep neural network requires a large amount of original data. This paper designs a flower classification model that combines generative adversarial network and ResNet-101 transfer learning algorithm, and uses stochastic gradient descent algorithm to optimize the training process of the model. The experimental results on the the international public flower recognition dataset, Oxford flower-102 dataset, show that by enhancing the original data, the accuracy of the network's recognition and classification of flowers is improved. At the same time, the model proposed in this paper is superior to other traditional network models, with higher recognition accuracy and robustness.

[1]  Jichun Wu,et al.  Deep Convolutional Encoder‐Decoder Networks for Uncertainty Quantification of Dynamic Multiphase Flow in Heterogeneous Media , 2018, Water Resources Research.

[2]  Nicholas Zabaras,et al.  Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification , 2018, J. Comput. Phys..

[3]  Fadzilah Siraj,et al.  Digital Image Classification for Malaysian Blooming Flower , 2010, 2010 Second International Conference on Computational Intelligence, Modelling and Simulation.

[4]  Wenjing Qi,et al.  Flower classification based on local and spatial visual cues , 2012, 2012 IEEE International Conference on Computer Science and Automation Engineering (CSAE).

[5]  Takeshi Saitoh,et al.  Automatic recognition of blooming flowers , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[6]  Andrew Zisserman,et al.  Automated Flower Classification over a Large Number of Classes , 2008, 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing.

[7]  I. Gogul,et al.  Flower species recognition system using convolution neural networks and transfer learning , 2017, 2017 Fourth International Conference on Signal Processing, Communication and Networking (ICSCN).

[8]  Louis J. Durlofsky,et al.  A New Data-Space Inversion Procedure for Efficient Uncertainty Quantification in Subsurface Flow Problems , 2017, Mathematical Geosciences.