基于阈值分割法和卷积神经网络的图像识别算法

针对传统卷积神经网络严重依赖数据量的问题, 提出一种基于均值迭代阈值分割法和卷积神经网络的图像识别算法, 通过均值迭代阈值分割法过滤图像背景, 并基于AlexNet构造新的卷积神经网络. 与其他常用的卷积神经网络进行对比实验结果表明, 在样本数量不足的图像识别任务中, 该算法识别效果较理想, 与其他卷积神经网络相比, 具有更高的识别准确度、 更低的识别误差和更快的收敛速度.

Image Recognition Algorithm Based on Threshold Segmentation Method and Convolutional Neural Network

Aiming at the problem that traditional convolutional neural network relied heavily on the amount of data, we proposed an image recognition algorithm based on mean iterative threshold segmentation method and convolutional neural network. The image background was filtered by means iterative threshold segmentation method, and a new convolutional neural network was constructed based on AlexNet. Compared with other commonly used convolutional neural networks, the experimental results show that the recognition effect of the proposed algorithm is ideal in image recognition tasks with insufficient samples, and it has higher recognition accuracy, lower recognition error and faster convergence rate than that of other convolutional neural networks.