一种多尺度嵌套卷积神经网络模型

Multi-Scale Convolutional Neural Network Model with Multilayer Maxout Networks

卷积神经网络模型要求训练图像与测试图像在空间尺度上一致.为弱化这一限制,对卷积层特征提取器进行多尺度改进,提出了一种尺度不变卷积神经网络模型,以自动适应输入图像在平面空间上的尺度变化.同时,将多层Maxout网络嵌入新模型中,以进一步提高特征提取能力,提高图像识别与分类的准确性.实验测试结果表明,该模型提高了传统卷积神经网络模型的尺度不变性和分类精度.

Convolution neural network models require the consistency of spatial scales between training images and testing images. In order to alleviate this restriction,a scale invariant convolution neural network model with multi-scale feature extractor was proposed,which can adapt to the in-plane scale change of input images. Meanwhile,multi-layer Maxout networks are nested into the model in order to improve the ability of feature extraction,so as to improve the accuracy of image recognition and classification. Experiments show that the new model improves the scale invariance and classification accuracy of traditional convolution neural networks.