Aimed at the problem of lacking of emotions images will influence the performance of CNN seriously, a large image emotion dataset, Large-scale deep emotion (LSDE) dataset, is built using semi-supervised dynamic method to ensure emotion labels of images. In order to bridge the gap and find the relationship between different levels features effectiveness, at first, objects of images are divided from complete images using salient object detection, then a relationship learning network is adopted based on foreground images and background images in this paper to learn relationships between different levels features of images to bridge the gap between image features and emotions of human beings. Experimental results in LSDE dataset, Twitter2 dataset and ArtPhoto dataset show that relationship learning network can extract different level features from foreground and background images and can learn the relationship between different level features and bridge the gap between features of images and emotions of human beings, the image emotion recognition system also can recognize emotions of images accurately.

image emotion recognition; multi-levels image features; relationship learning network; CNN; artificial intelligence