Abstract: Images exist widely in daily life, and image classification is of great practical significance. Aiming at the problems of low classification accuracy and high computational complexity in current multi-label image classification due to the complexity of the neural network model and the insufficient of extracted image feature information, a multi-label classification method combined CNN and interactive features, namely MLCNN-IF model, is proposed. The model is mainly divided into two steps. Firstly, a lightweight neural network （MLCNN） with only 9 layers is built with reference to the basic structure of traditional CNN, which is used to process image data and extract features. Secondly, based on the features extracted by MLCNN, the combined features of independent features are generated by the interactive feature method, so as to obtain a new and richer feature set. The experimental results show that compared with AlexNet, GoogLeNet and VGG16, the proposed model achieves better classification results on four multi-label image datasets, and its accuracy and precision rate are increased by 9% and 4.8% respectively on average. At the same time, the MLCNN network structure is relatively simpler, which effectively reduces the amount of model parameters and time complexity.

Key words: convolutional neural network, multi-label learning, deep learning, image classification, interactive feature