{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/building-emotional-machines-recognizing-image","title":"Building Emotional Machines: Recognizing Image Emotions through Deep Neural Networks","arxiv_id":"1705.07543","date":"2017-05-22","proceeding":null,"authors":["Hye-Rin Kim","Yeong-Seok Kim","Seon Joo Kim","In-Kwon Lee"],"abstract":"An image is a very effective tool for conveying emotions. Many researchers\nhave investigated in computing the image emotions by using various features\nextracted from images. In this paper, we focus on two high level features, the\nobject and the background, and assume that the semantic information of images\nis a good cue for predicting emotion. An object is one of the most important\nelements that define an image, and we find out through experiments that there\nis a high correlation between the object and the emotion in images. Even with\nthe same object, there may be slight difference in emotion due to different\nbackgrounds, and we use the semantic information of the background to improve\nthe prediction performance. By combining the different levels of features, we\nbuild an emotion based feed forward deep neural network which produces the\nemotion values of a given image. The output emotion values in our framework are\ncontinuous values in the 2-dimensional space (Valence and Arousal), which are\nmore effective than using a few number of emotion categories in describing\nemotions. Experiments confirm the effectiveness of our network in predicting\nthe emotion of images.","url_abs":"http://arxiv.org/abs/1705.07543v2","url_pdf":"http://arxiv.org/pdf/1705.07543v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"building-emotional-machines-recognizing-image","repo_url":"https://github.com/pohlinwei/AComPianist","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"object","task_name":"Object"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1705.07543","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}