{"title":"基于深度神经网络的图像情感识别","authors":"Bo Li, ChengCheng Guo, Hui Ren","doi":"10.1109/IICSPI.2018.8690404","DOIUrl":null,"url":null,"abstract":"Images can convey rich semantics and induce various emotions to viewers. Several studies have been introduced recently that apply the deep learning technology to predict image emotion. In this paper, by extracting and combing the different levels of features, we build an emotion classification model based on feed forward deep neural network to classify image emotion. Experiments confirm the effectiveness of our network in predicting the emotion of images.","PeriodicalId":6673,"journal":{"name":"2018 IEEE International Conference of Safety Produce Informatization (IICSPI)","volume":"36 1","pages":"561-564"},"PeriodicalIF":0.0000,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Image Emotion Recognition Based on Deep Neural Network\",\"authors\":\"Bo Li, ChengCheng Guo, Hui Ren\",\"doi\":\"10.1109/IICSPI.2018.8690404\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Images can convey rich semantics and induce various emotions to viewers. Several studies have been introduced recently that apply the deep learning technology to predict image emotion. In this paper, by extracting and combing the different levels of features, we build an emotion classification model based on feed forward deep neural network to classify image emotion. Experiments confirm the effectiveness of our network in predicting the emotion of images.\",\"PeriodicalId\":6673,\"journal\":{\"name\":\"2018 IEEE International Conference of Safety Produce Informatization (IICSPI)\",\"volume\":\"36 1\",\"pages\":\"561-564\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE International Conference of Safety Produce Informatization (IICSPI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IICSPI.2018.8690404\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE International Conference of Safety Produce Informatization (IICSPI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IICSPI.2018.8690404","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Image Emotion Recognition Based on Deep Neural Network
Images can convey rich semantics and induce various emotions to viewers. Several studies have been introduced recently that apply the deep learning technology to predict image emotion. In this paper, by extracting and combing the different levels of features, we build an emotion classification model based on feed forward deep neural network to classify image emotion. Experiments confirm the effectiveness of our network in predicting the emotion of images.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
