Extracting salient region for pornographic image detection
•A novel approach for ROI detection in pornographic images is put forward.•A ROI-based codebook algorithm is proposed.•A hybrid approach of pornographic image detection is explored. Content-based pornographic image detection, in which region-of-interest (ROI) plays an important role, is effective to...
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| Published in | Journal of visual communication and image representation Vol. 25; no. 5; pp. 1130 - 1135 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
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Amsterdam
Elsevier Inc
01.07.2014
Elsevier |
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| Abstract | •A novel approach for ROI detection in pornographic images is put forward.•A ROI-based codebook algorithm is proposed.•A hybrid approach of pornographic image detection is explored.
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. |
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| AbstractList | •A novel approach for ROI detection in pornographic images is put forward.•A ROI-based codebook algorithm is proposed.•A hybrid approach of pornographic image detection is explored.
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. 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. |
| Author | Yan, Chenggang Clarence Yin, Jian Liu, Yizhi Xie, Hongtao Liao, Zhuhua |
| Author_xml | – sequence: 1 givenname: Chenggang Clarence surname: Yan fullname: Yan, Chenggang Clarence organization: Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China – sequence: 2 givenname: Yizhi surname: Liu fullname: Liu, Yizhi email: liuyizhi928@gmail.com organization: School of Computer Science and Engineering, Hunan University of Science and Technology, China – sequence: 3 givenname: Hongtao surname: Xie fullname: Xie, Hongtao organization: Institute of Information Engineering, Chinese Academy of Sciences, National Engineering Laboratory for Information Security Technologies, Beijing, China – sequence: 4 givenname: Zhuhua surname: Liao fullname: Liao, Zhuhua organization: School of Computer Science and Engineering, Hunan University of Science and Technology, China – sequence: 5 givenname: Jian surname: Yin fullname: Yin, Jian organization: Department of Computer, Shandong University, Weihai, China |
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| Keywords | Codebook algorithm Skin-color model Salient region detection Bag-of-visual-words (BoVW) Speed up robust features (SURF) Region-of-interest (ROI) Visual attention analysis Pornographic image detection Computer vision Face recognition Pornography Modeling Interest region Bag of words Image analysis Experimental result Codebook Facies Stimulus salience Skin Visual attention Hessian matrices Content analysis |
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| Snippet | •A novel approach for ROI detection in pornographic images is put forward.•A ROI-based codebook algorithm is proposed.•A hybrid approach of pornographic image... Content-based pornographic image detection, in which region-of-interest (ROI) plays an important role, is effective to filter pornography. Traditionally,... |
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| SubjectTerms | Applied sciences Artificial intelligence Bag-of-visual-words (BoVW) Biological and medical sciences Codebook algorithm Coding, codes Color Computer science; control theory; systems Exact sciences and technology Face recognition Fundamental and applied biological sciences. Psychology Fuses Human Image detection Information, signal and communications theory Pattern recognition. Digital image processing. Computational geometry Perception Pornographic image detection Pornography Psychology. Psychoanalysis. Psychiatry Psychology. Psychophysiology Region-of-interest (ROI) Representations Salient region detection Signal and communications theory Skin-color model Speed up robust features (SURF) Telecommunications and information theory Vision Visual Visual attention analysis |
| Title | Extracting salient region for pornographic image detection |
| URI | https://dx.doi.org/10.1016/j.jvcir.2014.03.005 https://www.proquest.com/docview/1551065066 |
| Volume | 25 |
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