基于抽象关系场景图的图像情感识别OA北大核心CSTPCD

Image sentiment recognition is an abstract process of forecasting human emotions by analysis of various visu-al stimuli.Most of the earlier literature does not focus on the relationships among objects and the interactions between objects and scenes,and the complex and diverse relationships among objects are difficult to fully exploit,resulting in difficulty in correctly forecasting image sentiment.To deal with this problem,we develop an abstract relational scene graph network for image sentiment recognition.First,an object and attribute detector is generated to extract object fea-tures and their corresponding attribute features from images.Second,the affinities and abstract relationship features among objects are inferred through object features,and then the abstract relational scene graph is generated.Moreover,an abstract relational graph convolutional network is developed for reasoning the abstract relational scene graph.Last,a progressive attention mechanism is designed to fuse multiple object features to acquire the overall object feature of the image.Application on three public datasets,FI,EmotionRoI,and Twitter I,demonstrates that the classification accuracy of the proposed method is better than that of the existing methods.