Automatic System for Filtering Obscene Video

Kim, Chang-Yul; Kwon, Oh-Jin; Kim, Won-Gyu; Choi, Seokrim

doi:10.1109/icact.2008.4494034

Cited by 28 publications

(23 citation statements)

References 6 publications

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“…By means of these techniques it is possible to identify and block adult content before it reaches the wrong audiences. A number of solutions exist for adult image detection [2,3,4,5,6], and fewer have been proposed to deal with video mainly extending the image-based methods [7,8,9] or using a more sophisticated set of features exploiting the temporal cues in video [10,11,12]. Taking into account that adult content detection is a video classification problem, a number of interesting and promising methods to be specifically applied for the mentioned purpose are reviewed [13,14,15,16,17,18].…”

Section: And Thementioning

confidence: 99%

“…The most typical means of identifying pornography are strongly based in skin detection [21,6,22]. In the case of videos, the most common approach is basically an extension of the image detection techniques [7,8,9] applied for a set of selected frames representative of the clip known as key frames. More novel techniques utilize the temporal cues as well to extract information such as motion patterns [10,11,12].…”

Section: Literature Reviewmentioning

confidence: 99%

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Adult Video Content Detection Using Machine Learning Techniques

Ochoa

Yayilgan

Cheikh

2012

2012 Eighth International Conference on Signal Image Technology and Internet Based Systems

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Automatic adult video detection is a problem of interest for many organizations around the globe aiming to restrict the availability of potentially harmful material for young audiences. Being most of the existing techniques a mere extension of the image categorization problem. In the present work we employ video genre classification techniques applied specifically for adult content detection by considering cinematic principles. Shot structure and camera motion in the temporal domain are used as the main features while skin detection and color histograms representation in the spatial domain are utilized as complementary features. Using a data set of more than 7 hours of video, our experiments comparing two different SVM algorithms achieve a high accuracy of 94.44%.

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Section: And Thementioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Adult Video Content Detection Using Machine Learning Techniques

Ochoa

Yayilgan

Cheikh

2012

2012 Eighth International Conference on Signal Image Technology and Internet Based Systems

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“…Kim et al [7] first extracted the frames of a video file and detected shot boundaries or key frames. Then they calculated motion vectors and checked if the frame had a global motion or not.…”

Section: Methodsmentioning

confidence: 99%

“…Existing method for obscene video detection may be roughly divided into three groups including (1) methods based on spatial information of video frames [5][6][7], (2) methods based on motion vectors [8,9], and (3) methods based on spatiotemporal features [10][11][12]. Wang et al [5] used a three-step method for identifying illicit videos.…”

Section: Methodsmentioning

confidence: 99%

Content-based obscene video recognition by combining 3D spatiotemporal and motion-based features

et al. 2012

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In this article, a new method for the recognition of obscene video contents is presented. In the proposed algorithm, different episodes of a video file starting by key frames are classified independently by using the proposed features. We present three novel sets of features for the classification of video episodes, including (1) features based on the information of single video frames, (2) features based on 3D spatiotemporal volume (STV), and (3) features based on motion and periodicity characteristics. Furthermore, we propose the connected components' relation tree to find the spatiotemporal relationship between the connected components in consecutive frames for suitable features extraction. To divide an input video into video episodes, a new key frame extraction algorithm is utilized, which combines color histogram of the frames with the entropy of motion vectors. We compare the results of the proposed algorithm with those of other methods. The results reveal that the proposed algorithm increases the recognition rate by more than 9.34% in comparison with existing methods.

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“…In [12], after extracting video frames, motion vectors were calculated and employed to segment video frames to frames with global or local motions. For local motion, skin regions were extracted and edge moments were utilized to classify each key frame as an objectionable or a benign frame.…”

Section: Introductionmentioning

confidence: 99%

Obscene Video Recognition Using Fuzzy SVM and New Sets of Features

Behrad

Salehpour

Saiedi

et al. 2013

International Journal of Advanced Robotic Systems

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In this paper, a novel approach for identifying normal and obscene videos is proposed. In order to classify different episodes of a video independently and discard the need to process all frames, first, key frames are extracted and skin regions are detected for groups of video frames starting with key frames. In the second step, three different features including 1-structural features based on single frame information, 2-features based on spatiotemporal volume and 3-motion-based features, are extracted for each episode of video. The PCA-LDA method is then applied to reduce the size of structural features and select more distinctive features. For the final step, we use fuzzy or a Weighted Support Vector Machine (WSVM) classifier to identify video episodes. We also employ a multilayer Kohonen network as an initial clustering algorithm to increase the ability to discriminate between the extracted features into two classes of videos. Features based on motion and periodicity characteristics increase the efficiency of the proposed algorithm in videos with bad illumination and skin colour variation. The proposed method is evaluated using 1100 videos in different environmental and illumination conditions. The experimental results show a correct recognition rate of 94.2% for the proposed algorithm.

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Automatic System for Filtering Obscene Video

Cited by 28 publications

References 6 publications

Adult Video Content Detection Using Machine Learning Techniques

Adult Video Content Detection Using Machine Learning Techniques

Content-based obscene video recognition by combining 3D spatiotemporal and motion-based features

Obscene Video Recognition Using Fuzzy SVM and New Sets of Features

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