基于双重优化的卷积神经网络图像识别算法

Convolutional Neural Network Algorithm Based on Double Optimization for Image Recognition

为了进一步提高卷积神经网络算法的收敛速度和识别精度,提出基于双重优化的卷积神经网络图像识别算法.在构建卷积神经网络的过程中,针对特征提取和回归分类建立双重优化模型,实现对卷积与全连接过程的集成优化,并与局部优化算法对比,分析各算法的识别率和收敛速度的差异.在手写数字集和人脸数据集上的实验表明,双重优化模型可以在较大程度上提高卷积神经网络的收敛速度和识别精度,并且这种优化策略可以进一步拓展到其它与卷积神经网络相关的深度学习算法中.

To improve the recognition accuracy and the convergence speed of the convolutional neural network algorithm, a convolutional neural network algorithm based on double optimization is proposed. By modeling a convolutional neural network and optimizing the process of feature extraction and regression classification, an optimization convolutional neural network is built. Thus, the integrated optimization of the convolution and the full-connection process is realized. Compared with the local optimization network, the integrated optimization network obtains a higher convergence speed and better recognition accuracies. The experiments are conducted based on handwritten digit datasets and face datasets and the results show the improvement of the convergence speed and the recognition accuracy. And the effectiveness of the proposed algorithm is demonstrated. Moreover, this optimization strategy can be further extended into other deep learning algorithms related to convolution neural networks.