Conference Paper

Constructing SURF visual-words for pornographic images detection

Lab. of Knowledge Process. & Networked Manuf., Hunan Univ. of Sci. & Technol., Xiangtan, China
DOI: 10.1109/ICCIT.2009.5407272 Conference: Computers and Information Technology, 2009. ICCIT '09. 12th International Conference on
Source: IEEE Xplore

ABSTRACT

Pornographic images detection is necessary for us to filter out objectionable information on the Internet. Bag-of-visual-words (BoVW) based pornographic images detection is promising because it can compensate the defect of the traditional approach. However, there are many choices to construct visual-words which are crucial to the tradeoff between the speed and the performance. We propose a novel method of constructing SURF (speeded up robust features) visual-words in skin regions and combining it with color moments. The results show that the performance of SURF visual-words is better than that of SIFT (scale-invariant feature transform) visual-words and our method is more effective to detect pornographic images than many existing methods.

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Available from: Yizhi Liu
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    • "Addressing aforementioned problems, we propose a novel approach of extracting salient region for pornographic image detection based on our previous works [18] [19] [24]. Our contributions lie in: (1) We put forward a novel approach for ROI detection in pornographic images. "

    Full-text · Dataset · Apr 2015
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    • "But the algorithm lacks optimization itself, and needs much computing time. [11] proposed to build SURF visual vocabulary to extract local feature descriptors of the skin-color region so that we can reduce the computation time of SIFT algorithm [12], although the computational efficiency of SURF has improved greatly than SIFT algorithm, but the clustering process using a simple K-Mean clustering algorithm, resulting the lack of semantics. Both methods for target identifying all exists flaw. "

    Preview · Article · Jan 2015
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    • "Addressing aforementioned problems, we propose a novel approach of extracting salient region for pornographic image detection based on our previous works [18] [19] [24]. Our contributions lie in: (1) We put forward a novel approach for ROI detection in pornographic images. "
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    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.
    Full-text · Article · Jul 2014 · Journal of Visual Communication and Image Representation
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