Extracting salient region for pornographic image detection

• Published in: Journal of visual communication and image representation Vol. 25; no. 5; pp. 1130 - 1135
• Main Authors: Yan, Chenggang Clarence, Liu, Yizhi, Xie, Hongtao, Liao, Zhuhua, Yin, Jian
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Inc 01.07.2014; Elsevier
• Subjects: 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; 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
• ISSN: 1047-3203; 1095-9076
• DOI: 10.1016/j.jvcir.2014.03.005

•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.