Research on image retrieval using deep convolutional neural network combining L1 regularization and PRelu activation function

3rd International Conference on Advances in Energy, Environment and Chemical Engineering

能源学;生态环境科学;化学工业

Qingjie, Wei^1 ; Wenbin, Wang^1

Chongqing University of Posts and Telecommunications, China^1

关键词: Activation functions;    Deep convolutional neural networks;    Direct extraction;    Human brain;    L1 regularizations;    Overfitting;    Retrieval accuracy;    Visual feature;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/69/1/012156/pdf
DOI  :  10.1088/1755-1315/69/1/012156

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

In this paper, the image retrieval using deep convolutional neural network combined with regularization and PRelu activation function is studied, and improves image retrieval accuracy. Deep convolutional neural network can not only simulate the process of human brain to receive and transmit information, but also contains a convolution operation, which is very suitable for processing images. Using deep convolutional neural network is better than direct extraction of image visual features for image retrieval. However, the structure of deep convolutional neural network is complex, and it is easy to over-fitting and reduces the accuracy of image retrieval. In this paper, we combine L1 regularization and PRelu activation function to construct a deep convolutional neural network to prevent over-fitting of the network and improve the accuracy of image retrieval.