自适应增强卷积神经网络图像识别

目的 为了进一步提高卷积神经网络的收敛性能和识别精度，增强泛化能力，提出一种自适应增强卷积神经网络图像识别算法。方法 构建自适应增强模型，分析卷积神经网络分类识别过程中误差产生的原因和误差反馈模式，针对分类误差进行有目的地训练，实现分类特征基于迭代次数和识别结果的自适应增强以及卷积神经网络权值的优化调整。自适应增强卷积神经网络与多种算法在收敛速度和识别精度等性能上进行对比，并在多种数据集上检测自适应卷积神经网络的泛化能力。结果 通过对比实验可知，自适应增强卷积神经网络算法可以在很大程度上优化收敛效果，提高收敛速度和识别精度，收敛时在手写数字数据集上的误识率可降低20.93%，在手写字母和高光谱图像数据集上的误识率可降低11.82%和15.12%；与不同卷积神经网络优化算法对比，误识率比动态自适应池化算法和双重优化算法最多可降低58.29%和43.50%；基于不同梯度算法的优化，误识率最多可降低33.11%；与不同的图像识别算法对比，识别率也有较大程度提高。结论 实验结果表明，自适应增强卷积神经网络算法可以实现分类特征的自适应增强，对收敛性能和识别精度有较大的提高，对多种数据集有较强的泛化能力。这种自适应增强模型可以进一步推广到其他与卷积神经网络相关的深度学习算法中。

Adaptively enhanced convolutional neural network algorithm for image recognition

Objective Deep learning has been widely used in computer vision and possesses increased number of network layers, which is its major difference from shallow learning. Deep learning can learn data through multi-level networks, construct a complex nonlinear function model to extract data features, combine low-level features into high-level features, and complete the classification and recognition of data. Deep learning can extract accurate features and avoid the subjectivity and randomness of artificial selection without human participation in the process of feature extraction. Convolutional neural network (CNN) is an important model of deep learning and is widely used in image classification and recognition tasks. Improving the convergence speed and recognition rate can promote the application development of CNN. CNN possesses strong robustness because of its convolution and pooling operation during the feature extraction phase. It also exhibits powerful capability of learning owing to its multiple layers and rich parameters. Many researchers have improved the CNN for its application in different fields. In this study, an adaptively enhanced CNN algorithm is proposed to improve the convergence speed and recognition accuracy of the CNN, reduce the difficulty of training, optimize the convergence effect, and enhance the generalization capability. Method CNN mainly includes forward and back propagations for classifying and recognizing images. Forward propagation includes feature extraction and target classification, and back propagation includes feedback of classification error and updating of weights. The proposed algorithm is aimed at adding an error adaptively enhanced process between forth and back propagations, building the adaptively enhanced model, constructing the CNN on the basis of the adaptively enhanced model, analyzing the causes of error classification and error feedback pattern during the process of CNN classification and recognition, and training the classification error purposefully. The two largest values in the classification results are extracted as features, and their corresponding errors are enhanced whereas other error values remain unchanged. The classification features and weights of the CNN can be enhanced adaptively with iterations, and the results of classification can accelerate the convergence of the CNN and improve the recognition rate. The optimization degree of adaptively enhanced model for convergence speed, recognition accuracy, and convergence effect as well as generalization capability of the CNN are compared with those of other algorithms. The performance of adaptively enhanced CNN in terms of generalization capability is validated on various datasets. Notably, computing each algorithm is time consuming. Result The experiments are carried out on datasets of handwritten digital numbers, handwritten characters, and hyperspectral images, and the results of different image recognition and optimization algorithms based on CNN on these datasets are compared. The contrast experimental results show that the adaptively enhanced CNN algorithm can improve the convergence speed and recognition rate in a large extent and can optimize the convergence effect and generalization capability. The error rate of recognition can be reduced by 20.93% on handwritten digital numbers, 11.82% on handwritten characters, and 15.12% on hyper spectral images when it converges. The adaptively enhanced CNN presents no increase in time cost. The proposed algorithm also possesses better recognition effect than that of other CNN optimization algorithms. For example, the error rate of recognition can be reduced by 58.29% and 43.50% at most compared with the rates obtained by dynamic adaptive pooling algorithm and dual optimization algorithm. The proposed algorithm can improve the effect of different gradient optimization algorithms by reducing the error rate of recognition by 33.11% at most. This algorithm also presents different improvements in recognition rate compared with other image recognition algorithms. Conclusion The adaptively enhanced CNN can enhance the classification feature adaptively. Improvements in convergence speed, recognition rate, and optimization of convergence effect are demonstrated. The CNN can be improved effectively by the adaptively enhanced model without increasing the cost of time. In addition, the proposed algorithm can achieve the optimization effect by use of the different gradient descent algorithms and can be further optimized on the basis of the gradient descent algorithms. The adaptively enhanced CNN exhibits good generalization capability. This algorithm can be further extended to other deep learning algorithms related to CNN.