{"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/smart-content-recognition-from-images-using-a","title":"Smart Content Recognition from Images Using a Mixture of Convolutional Neural Networks","arxiv_id":"1612.09506","date":"2016-12-30","proceeding":null,"authors":["Tee Connie","Mundher Al-Shabi","Michael Goh"],"abstract":"With rapid development of the Internet, web contents become huge. Most of the\nwebsites are publicly available, and anyone can access the contents from\nanywhere such as workplace, home and even schools. Nevertheless, not all the\nweb contents are appropriate for all users, especially children. An example of\nthese contents is pornography images which should be restricted to certain age\ngroup. Besides, these images are not safe for work (NSFW) in which employees\nshould not be seen accessing such contents during work. Recently, convolutional\nneural networks have been successfully applied to many computer vision\nproblems. Inspired by these successes, we propose a mixture of convolutional\nneural networks for adult content recognition. Unlike other works, our method\nis formulated on a weighted sum of multiple deep neural network models. The\nweights of each CNN models are expressed as a linear regression problem learned\nusing Ordinary Least Squares (OLS). Experimental results demonstrate that the\nproposed model outperforms both single CNN model and the average sum of CNN\nmodels in adult content recognition.","url_abs":"http://arxiv.org/abs/1612.09506v2","url_pdf":"http://arxiv.org/pdf/1612.09506v2.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":"smart-content-recognition-from-images-using-a","repo_url":"https://github.com/mundher/NSFW","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"smart-content-recognition-from-images-using-a","repo_url":"https://github.com/jackaduma/nude-detect","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[{"method_slug":"linear-regression","method_name":"Linear Regression"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1612.09506","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}