Abstract In scene classification based on convolutional neural network, over-fitting is caused due to the large number of network training and poor convergence performance with the small training dataset. To eliminate the negative effect, an algorithm for scene classification with adaptive learning rate and sample training mode is proposed. The network learning rate is adaptively adjusted on the framework of convolutional neural network according to the variation of the error function in the network training. When the error function changes slightly, the learning rate of the batch is unchanged. When the error function changes more remarkably, the variation of the learning rate is inversely proportional to the variation of the error function. Meanwhile, according to the network output, the sample training mode is switched, and the inaccurately recognized images are emphatically trained. The experimental results on Scene-15 and Cifar-10 scene datasets show that the proposed method improves the convergence of neural networks and effectively improves the classification accuracy, especially the classification accuracy of complex scenes such as indoor scenes.
Fund:Supported by National Natural Science Foundation of China(No.61663031、61661036), Key Research and Development Plan Project of Jiangxi Province(No.20161BBE50085)
Corresponding Authors:
CHU Jun(Corresponding author), Ph.D., professor. Her research interests include image processing and analysis, pattern recognition and computer vision.
About author:: SU Yawei, master student. His research interests include image classification and deep learning.WANG Lu, master, lecturer. Her research interests include image processing and pattern recognition.
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