Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (6): 176-179.doi: 10.11896/jsjkx.190600142

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Flower Image Enhancement and Classification Based on Deep Convolution Generative Adversarial Network

YANG Wang-gong, HUAI Yong-jian   

  1. School of Information,Beijing Forestry University,Beijing 100083,China
  • Received:2019-06-25 Online:2020-06-15 Published:2020-06-10
  • About author:YANG Wang-gong,born in 1982,Ph.D.His main research interests include machine learning,virtual reality and digital entertainment.
    HUAI Yong-jian,born in 1970,professor,Ph.D supervisor,is a member of China Computer Federation.His main research interests include virtual reality and virtual landscape,data visualization and somatosensory interaction techno-logy.
  • Supported by:
    This work was supported by the General Program of National Natural Science Foundation of China (31770589) and Construction Project of Scientific Research Team in Central Colleges and Universities (2015ZCQ-XX).

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Abstract: In order to improve the accuracy of flower image recognition and classification,an algorithm based on deep convolution to generate a network is used to identify and classify flower images.In order to ensure the feature integrity of the flower image during the convolution process,the real flower images with different sizes are quantitatively averaged,the size of the block size is ignored,the number of blocks is equalized,and then the image of the block is deeply convolved.The pooling is enhanced,the enhancement method is the maximum value enhancement,and the noise is generated by the maximum pool.Then the two are compared and discriminated.The cross-entropy error is used to evaluate the value function to solve the flower image recognition and classification results.In this paper,the image enhancement of flowers,the image recognition of similar flowers and the classification of different flower images are simulated respectively.It is proved by experiments that the algorithm has obvious advantages and good stability in the classification accuracy of flower images.

Key words: Deep convolution, Flower image, Generative adversarial network, Maximum pooling, Value function

CLC Number: 

  • TP391.4
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