In this work, we investigate a method for detection of nudity in videos based on a bag-of-visual-feature representation for frames and an associated voting scheme. Our results showed that our 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 a value of 93.2% of correct classification. Also, to support this task, we developed our own database of nude and non-nude videos.
The database of nude and non-nude videos contains a collection of 179 video segments collected from the following movies: Alpha Dog, Basic Instinct, Before The Devil Knows You're Dead, Cashback, Eros, Les Anges Exterminateurs, Loner, Original Sin, Primer, Striptease and The Bubble. For nude samples, longer sequences were partitioned into shorter ones. The sequences for the non-nude class were collected by randomly selecting the initial time and length. Random selections which felt inside a nude scene were discarded. Those random selections were performed on the same movies above listed for the nude class.
A summary of the database.
Number of video segments per video.
THIS DATABASE IS PROVIDED "AS IS" AND WITHOUT ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, WITHOUT LIMITATION, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. The video segments provided were produced by third-parties, who may have retained copyrights. They are provided strictly for non-profit research purposes, and limited, controlled distributed, intended to fall under the fair-use limitation. We take no guarantees or responsibilities, whatsoever, arising out of any copyright issue. Use at your own risk.
The database contains 179 video segments (90 segments of non-nude videos and 89 segments of nude videos). To download the data, please click here. Please feel free to contact me if you have any questions or comments.
Results @ SIBGRAPI 2009
The experiments were designed to evaluate the ability of discriminating between nude and non-nude videos from BoVFs representations for the selected frames. Two different sample ratios were tested: 1/15 frame (2 frames per second) and 1/30 frames (1 frame per second). Three different vocabulary sizes were tested for classification of those BoVF vectors between nude and non-nude: 60, 120 and 180. A linear SVM classifier was used, and its penalty error parameter (C) was refined by the procedure described in LIBSVM guide. In each step, the classification rate was measured in a 5-fold cross validation scheme.
Thus, the recognition rate for frames (second column) are those expected if only a keyframe was selected to represent the entire video segment. In the third column, are the recognition rates using the proposed voting scheme. From those tables, it is possible to see that, in all cases, applying the voting algorithm causes a statistically significant increase in the overall recognition rate for the videos, when compared to the recognition rate of the individual frames. Such results indicate that, indeed, the voting scheme is able to take advantage of the existence of several similar frames to solve some dubious cases.
If you make use of our database, please cite the following reference:
See alsoNude detection in images
This work is supported by Brazilian research funding agencies: CAPES, CNPq and FAPEMIG.