Extracting salient region for pornographic image detection |
| |
Institution: | 1. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;2. School of Computer Science and Engineering, Hunan University of Science and Technology, China;3. Institute of Information Engineering, Chinese Academy of Sciences, National Engineering Laboratory for Information Security Technologies, Beijing, China;4. Department of Computer, Shandong University, Weihai, China;1. Department of Electrical and Computer Engineering, Concordia University, Montréal, QC H3G 2W1, Canada;2. Concordia Institute for Information Systems Engineering, Concordia University, Montréal, QC H3G 2W1, Canada;1. Department of Electronic Engineering, The Chinese University of Hong Kong, Shatin N.T., Hong Kong;2. Hong Kong Applied Science and Technology Research Institute (ASTRI), Shatin N.T., Hong Kong |
| |
Abstract: | 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. |
| |
Keywords: | 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) |
本文献已被 ScienceDirect 等数据库收录! |
|