WANG Qing,LI Xiao-jie,CHEN Jun.Research on Image Classification based on HE-Net Convolutional Neural Networks[J].Journal of Chengdu University of Information Technology,2017,(05):503-507.[doi:10.16836/j.cnki.jcuit.2017.05.007]
基于He-Net的卷积神经网络算法的图像分类研究
- Title:
- Research on Image Classification based on HE-Net Convolutional Neural Networks
- 文章编号:
- 2096-1618(2017)05-0503-05
- Keywords:
- artificial intelligent; deep learning; Caffe frame; convolution neural network; image classification; activation function
- 分类号:
- TP301.6
- 文献标志码:
- A
- 摘要:
- 基于Caffe深度学习框架和首层卷积层取反操作,提出了He-Net 网络模型解决图像分类问题。该模型主要由3个卷积层和最大池化层及3个完全连接层组成,首层采用卷积层取反以增加有效特征信息的传递,卷积层采用较小的卷积核提取更多的纹理特征,最后采用Softmax分类器实现图像分类。为了使训练更快速,采用更加高效的GPU运算实现卷积操作。针对相同数据集,同经典的网络模型CaffeNet、AlexNet实验比较,He-Net网络模型具有更高的分类正确率。
- Abstract:
- Based on the Caffe deep learning framework and the first convolution layer inversion operation, this paper proposes a He-Net network model to achieve image classification problem. The model consists of three convolution layers and the max-pooling layer, and three fully connection layers. The first convolution layer adopts the inversion operation to add the transmission of the effective feature information and uses a smaller convolution kernel to extract more texture features. Finally, the softmax classifier is employed to identify image classifications. In order to make training faster, we use a more efficient GPU to achieve convolution operations. For the same data set, compared with the classic network model CaffeNet, AlexNet experiment, He-Net network model has a higher classification accuracy.
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备注/Memo
收稿日期:2017-05-06 基金项目:国家自然科学基金青年基金资助项目(61602066); 四川省教育厅资助项目(17ZA0063)