{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T12:43:24Z","timestamp":1740141804331,"version":"3.37.3"},"reference-count":21,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2014,3,1]],"date-time":"2014-03-01T00:00:00Z","timestamp":1393632000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Distributed Sensor Networks"],"published-print":{"date-parts":[[2014,3,1]]},"abstract":"<jats:p> This paper presents a method for detecting harmful images using an active shape model (ASM) in social network services (SNS). For this purpose, our method first learns the shape of a woman's breast lines through principal component analysis and alignment, as well as the distribution of the intensity values of the corresponding control points. This method then finds actual breast lines with a learned shape and the pixel distribution. In this paper, to accurately select the initial positions of the ASM, we attempt to extract its parameter values for the scale, rotation, and translation. To obtain this information, we search for the location of the nipple areas and extract the location of the candidate breast lines by radiating in all directions from each nipple position. We then locate the mean shape of the ASM by finding the scale and rotation values with the extracted breast lines. Subsequently, we repeat the matching process of the ASM until saturation is reached. Finally, we determine objectionable images by calculating the average distance between each control point in a converged shape and a candidate breast line. <\/jats:p>","DOI":"10.1155\/2014\/673721","type":"journal-article","created":{"date-parts":[[2014,3,25]],"date-time":"2014-03-25T04:02:07Z","timestamp":1395720127000},"page":"673721","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["ASM-Based Objectionable Image Detection in Social Network Services"],"prefix":"10.1177","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2304-0403","authenticated-orcid":false,"given":"Sung-Il","family":"Joo","sequence":"first","affiliation":[{"name":"Department of Media, Soongsil University, 369, Sangdo-Ro, Dongjak-Gu, Seoul 156-743, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5580-4098","authenticated-orcid":false,"given":"Seok-Woo","family":"Jang","sequence":"additional","affiliation":[{"name":"Department of Digital Media, Anyang University, 708-113, Anyang 5-Dong, Manan-Gu, Anyang 430-714, Republic of Korea"}]},{"given":"Seung-Wan","family":"Han","sequence":"additional","affiliation":[{"name":"Video Surveillance Research Section, ETRI, 218, Gajeong-Ro, Yuseong-gu, Daejeon 305-700, Republic of Korea"}]},{"given":"Gye-Young","family":"Kim","sequence":"additional","affiliation":[{"name":"School of Computing, Soongsil University, 369, Sangdo-Ro, Dongjak-Gu, Seoul 156-743, Republic of Korea"}]}],"member":"179","published-online":{"date-parts":[[2014,3,24]]},"reference":[{"first-page":"349","volume-title":"Proceedings of the IEEE International Conference on Multimedia and Expo Workshops (ICMEW '12)","author":"Yin L.","key":"B1-2014-673721"},{"key":"B2-2014-673721","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2012.2195481"},{"first-page":"967","volume-title":"Proceedings of the 8th International Conference on Signal Image Technology and Internet Based Systems (SITIS '12)","author":"Ochoa V. 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