关键词: 图像分类, 图像识别, 卷积神经网络, 深度连接, 中间层

Abstract: To solve the problems that the traditional Convolution Neural Networks（CNN） models usually use the output of the last layer as a feature representation, and fail to make full use of the intermediate layers of models, a CNN model based on connecting intermediate layers （Intermediate Layers Connected-CNN, ILC-CNN） is presented. Firstly, the front convolutional layer, the middle convolutional layer and the terminal convolutional layer of AlexNet are connected to form a more representative feature by deep connection. Then, the features which represent the whole image are obtained by pooling layer and full connected layer. In addition, the validity of the features obtained from the intermediate layers is guaranteed by training through an assistant classifier. The experimental results show that the proposed model is effective in the task of image classification and recognition, features extracted from the model are more recognizable, and the proposed model has higher recognition accuracy than other models.

Key words: image classification, image recognition, convolutional neural networks, deep connection, intermediate layers