Images are one of the important sources of information in daily life.By analyzing the details of their expressed content,information resources can be more fully utilized.With the rapid development of information technology,con-ducting emotional analysis work on image modalities has become a major research hotspot.The main steps of image senti-ment analysis are:emotion feature extraction,emotion space selection,feature fusion,and emotion recognition classifica-tion.Most of the existing image sentiment analysis work inputs based on the overall image,which fails to fully leverage the emotional role of local features in the image.If the global and local features of an image cannot be distinguished,the global features of the image will become more sensitive when problems such as low clarity and high background noise occur.Feature extraction and recognition work will be severely disrupted,which will have a certain impact on the accuracy of sentiment analysis.In response to the shortcomings of current image sentiment analysis,this article proposes a method for image sentiment analysis based on foreground and background segmentation.This method uses YOLOv5 as the frame-work and introduces ConvNeXt module and AFF module for feature extraction and attention fusion,respectively.The experimental results show that compared with several popular image sentiment analysis methods,this method is more suitable for scenes containing more emotional and semantic information,and its performance has also been improved.
关键词
图像情感分析/前后景分割/特征融合/YOLOv5/局部特征/全局特征
Key words
image sentiment analysis/foreground and background segmentation/feature fusion/YOLOv5/local features/global features
维普
万方数据