An Intelligent Optimization Algorithm for Sentiment Classification of Scene Images in Big Data Era

摘要

Today, big data era has arrived. In the field of image processing, more and more digital images are becoming available. Finding a required image quickly has therefore become harder and harder. Using intelligent optimization algorithm to acquiring semantic information about images and realize automatic image classification is an effective technology that can be used to improve image retrieval performance. An intelligent optimization algorithm for sentiment classification of scene images is proposed in this paper. This method describes image emotions by integrating personality and mood factors. It uses the BP neural network to implement this process and solve the problem of semantic understanding during automatic image classification. Using 600 scene images downloaded from the Baidu photo channel to train and test this method, our experiments achieved good results when compared with those obtained from manual computing. Therefore, the algorithm proposed here can lay a solid foundation for the semantic classification of more types of images and possesses some practical value.