Down Image Recognition Based on Deep Convolution Neural Networks
[1]Yang Wenzhu,Liu Qing,Wang Sile,et al.Down Image Recognition Based on Deep Convolution Neural Networks[J].Journal of Zhengzhou University (Engineering Science),2018,39(02):11-17.[doi:10.13705/j.issn.1671-6833.2018.02.015]
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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
39卷
Number of periods:
2018 02
Page number:
11-17
Column:
Public date:
2018-03-30
- Title:
- Down Image Recognition Based on Deep Convolution Neural Networks
- Keywords:
- deep convolutional neural networks; weights initialization; sparse autoencoder; visual saliency; image recognition
- CLC:
- -
- DOI:
- 10.13705/j.issn.1671-6833.2018.02.015
- Abstract:
- Bcause of the scale and the various shapes of down in the image, it was difficult for traditional image recognition method to correctly recognize the type of down image and got the required recognition accuracy, even for the Traditional Convolutional Neural Networks (TCNN). To solve the above problems, a Deep Convolutional Neural Networks (DCNN) for down image recognition was constructed, and a new weight initialization method was proposed. Firstly, these salient regions of images were cut from the images using the visual saliency model.Then, these salient regions were used to train a sparse autoencoder and get a collection of convolutional filters, which accord with the statistical characteristics of dataset. At last, a DCNN with Inception module and its variants was constructed. To enhance the recognition accuracy, the depth of the network was deepened. The experiment results indicated that the constructed DCNN increased the recognition acuracy by 2.7% compared to TCNN, when recognizing the down in the images. The convergence rate of the proposed CNN with the new weight initialization method was improved by 25.5% compared to TCNN.
- References:
- -
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Last Update:
2018-04-01