A practical system for detecting obscene videos

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

doi:10.1109/tce.2011.5955203

Cited by 14 publications

(18 citation statements)

References 6 publications

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“…Also, the use of voting algorithm without the extraction of key frames makes the algorithm unsuitable for the classification of small video episodes with different categories. The algorithm of [15] also used spatial features based on Zernike moments to detect nudity in the video file. The approaches used the global motion in the video frames to group frames and reduce the processing time.…”

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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Section: Methodsmentioning

confidence: 99%

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

et al. 2012

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“…Furthermore, compared to homogeneous textures such as bricks or sands which have the uniform statistical features, inhomogeneous textures like clouds or flowers generally cannot be extracted robust texture features using conventional algorithms focusing on homogeneous textures [25]. In effectively making up the missed shape and space information of the holistic texture image when LBP texture features are extracted, Zernike moment is a desirable choice.…”

Section: Introductionmentioning

confidence: 99%

“…Moments and functions of moments have been successfully utilized as pattern features in many applications such as image recognition [26] and image retrieval [25] which can capture global information of the image. Zernike moments are deduced based on the theory of orthogonal polynomials.…”

Section: Introductionmentioning

confidence: 99%

Texture classification using rotation invariant models on integrated local binary pattern and Zernike moments

Wang

Zhao

Chen

2014

EURASIP J. Adv. Signal Process.

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More and more attention has been paid to the invariant texture analysis, because the training and testing samples generally have not identical or similar orientations, or are not acquired from the same viewpoint in many practical applications, which often has negative influences on texture analysis. Local binary pattern (LBP) has been widely applied to texture classification due to its simplicity, efficiency, and rotation invariant property. In this paper, an integrated local binary pattern (ILBP) scheme including original rotation invariant LBP, improved contrast rotation invariant LBP, and direction rotation invariant LBP is proposed which can effectively overcome the deficiency of original LBP that is ignoring contrast and direction information. In addition, for surmounting another major drawback of LBP such as locality which can result in the lack of shape and space expression of the holistic texture image, Zernike moment features are fused into the improved LBP texture features in the proposed method because they comprise orthogonal and rotation invariant property and can be easily and rapidly calculated to an arbitrary high order. Experimental results show that the proposed method can be remarkably superior to the other state-of-the-art methods when rotation invariant texture features are extracted and classified.

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“…Modeling the skin colour of humanbeings is very challenging task in image analysis since it is influenced by various factors such as illumination, camera characteristics, ethnicity, individual characteristics, subject appearance, background colour, shadow and motion etc., [2] [3].…”

Section: Introductionmentioning

confidence: 99%

“…Kakhamanu et al [28] have reviewed the methods for skin colour modeling and detection. Chang-Yul Kim et al [3] studied a practical system for detecting obscene videos. Ki-won Byun and Ki-Gon Nam [29] studied the skin region detection using mean shift algorithm based on the histogram approximation.…”

Section: Introductionmentioning

confidence: 99%

A Robust Skin Colour Segmentation Using Bivariate Pearson Type IIαα (Bivariate Beta) Mixture Model

Jagadesh¹,

Rao²,

Satyanarayana³

2012

IJIGSP

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Abstract-Probability distributions formulate the basic framework for developing several segmentation algorithms.Among the various segmentation algorithms, skin colour segmentation is one of the most important algorithms for human computer interaction. Due to various random factors influencing the colour space, there does not exist a unique algorithm which serve the purpose of all images. In this paper a novel and new skin colour segmentation algorithms is proposed based on bivariate Pearson type II a mixture model since the hue and saturation values always lies between 0 and 1. The bivariate feature vector of the human image is to be modeled with a Pearson type II a mixture (bivariate Beta mixture) model. Using the EM Algorithm the model parameters are estimated. The segmentation algorithm is developed under Bayesian frame. Through experimentation the proposed skin colour segmentation algorithm performs better with respect to segmentation quality metrics such as PRI, VOI and GCE. The ROC curves plotted for the system also revealed that the proposed algorithm can segment the skin colour more effectively than the algorithm with Gaussian mixture model for some images.Index Terms-Bivariate Pearson type II a mixture model, skin colour segmentation, HSI colour space, segmentation quality metrics

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A practical system for detecting obscene videos

Cited by 14 publications

References 6 publications

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

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

Texture classification using rotation invariant models on integrated local binary pattern and Zernike moments

A Robust Skin Colour Segmentation Using Bivariate Pearson Type IIαα (Bivariate Beta) Mixture Model

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