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Sign In. Access provided by: In this paper, we investigate a abstract for detection of nudity in videos based on a bag-of-visual-features representation for frames and an associated voting scheme.


Bag-of-Visual-Features BoVF approaches have been successfully applied to object recognition and scene classification, showing robustness to occlusion and also to the several kinds of variations that normally curse object detection methods. To the best of our knowledge, only two video in the literature use BoVF for nude detection in still images, and no other attempt has been made at applying BoVF nude videos.

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Nevertheless, the results of our experiments show that this approach is indeed able to provide good recognition rates for nudity even at the frame level and with a relatively low sampling ratio. Also, the proposed voting scheme significantly enhances the recognition rates for video segments, achieving, in the best abstract, a value of

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