标题：Extracting salient region for pornographic image detection
作者：Yan, Chenggang Clarence; Liu, Yizhi; Xie, Hongtao; Liao, Zhuhua; Yin, Jian
作者机构：[Yan, Chenggang Clarence] Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China.; [Liu, Yizhi; Liao, Zhuhua] Hunan Univ Sci & Technol, Sch C 更多
通讯作者地址：[Liu, YZ]Hunan Univ Sci & Technol, Sch Comp Sci & Engn, Xiangtan, Peoples R China.
来源：JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION
关键词：Salient region detection; Pornographic image detection; Visual attention; analysis; Region-of-interest (ROI); Skin-color model;; Bag-of-visual-words (BoVW); Codebook algorithm; Speed up robust features; (SURF)
摘要：Content-based pornographic image detection, in which region-of-interest (ROI) plays an important role, is effective to filter pornography. Traditionally, skin-color regions are extracted as ROI. However, skin-color regions are always larger than the subareas containing pornographic parts, and the approach is difficult to differentiate between human skins and other objects with the skin-colors. In this paper, a novel approach of extracting salient region is presented for pornographic image detection. At first, a novel saliency map model is constructed. Then it is integrated with a skin-color model and a face detection model to capture ROI in pornographic images. Next, a ROI-based codebook algorithm is proposed to enhance the representative power of visual-words. Taking into account both the speed and the accuracy, we fuse speed up robust features (SURF) with color moments (CM). Experimental results show that the precision of our ROI extraction method averagely achieves 91.33%, more precisely than that of using the skin-color model alone. Besides, the comparison with the state-of-the-art methods of pornographic image detection shows that our approach is able to remarkably improve the performance. (c) 2014 Elsevier Inc. All rights reserved.