文章基本信息

标题：ASM-Based Objectionable Image Detection in Social Network Services
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作者：Sung-Il Joo ; Seok-Woo Jang ; Seung-Wan Han 等
期刊名称：International Journal of Distributed Sensor Networks
印刷版ISSN：1550-1329
电子版ISSN：1550-1477
出版年度：2014
卷号：2014
DOI：10.1155/2014/673721
出版社：Hindawi Publishing Corporation
摘要：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.