ASM-Based Objectionable Image Detection in Social Network Services
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 inte...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-03-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2014/673721 |
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| _version_ | 1832553250121842688 |
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| author | Sung-Il Joo Seok-Woo Jang Seung-Wan Han Gye-Young Kim |
| author_facet | Sung-Il Joo Seok-Woo Jang Seung-Wan Han Gye-Young Kim |
| author_sort | Sung-Il Joo |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3495a00e4484498e806fad2c2c58ca82 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2014-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-3495a00e4484498e806fad2c2c58ca822025-02-03T05:54:32ZengWileyInternational Journal of Distributed Sensor Networks1550-14772014-03-011010.1155/2014/673721673721ASM-Based Objectionable Image Detection in Social Network ServicesSung-Il Joo0Seok-Woo Jang1Seung-Wan Han2Gye-Young Kim3 Department of Media, Soongsil University, 369, Sangdo-Ro, Dongjak-Gu, Seoul 156-743, Republic of Korea Department of Digital Media, Anyang University, 708-113, Anyang 5-Dong, Manan-Gu, Anyang 430-714, Republic of Korea Video Surveillance Research Section, ETRI, 218, Gajeong-Ro, Yuseong-gu, Daejeon 305-700, Republic of Korea School of Computing, Soongsil University, 369, Sangdo-Ro, Dongjak-Gu, Seoul 156-743, Republic of KoreaThis 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.https://doi.org/10.1155/2014/673721 |
| spellingShingle | Sung-Il Joo Seok-Woo Jang Seung-Wan Han Gye-Young Kim ASM-Based Objectionable Image Detection in Social Network Services International Journal of Distributed Sensor Networks |
| title | ASM-Based Objectionable Image Detection in Social Network Services |
| title_full | ASM-Based Objectionable Image Detection in Social Network Services |
| title_fullStr | ASM-Based Objectionable Image Detection in Social Network Services |
| title_full_unstemmed | ASM-Based Objectionable Image Detection in Social Network Services |
| title_short | ASM-Based Objectionable Image Detection in Social Network Services |
| title_sort | asm based objectionable image detection in social network services |
| url | https://doi.org/10.1155/2014/673721 |
| work_keys_str_mv | AT sungiljoo asmbasedobjectionableimagedetectioninsocialnetworkservices AT seokwoojang asmbasedobjectionableimagedetectioninsocialnetworkservices AT seungwanhan asmbasedobjectionableimagedetectioninsocialnetworkservices AT gyeyoungkim asmbasedobjectionableimagedetectioninsocialnetworkservices |