An intelligent categorization engine for bilingual web content filtering

Lee, P.Y.; Hui, Siu Cheung; Fong, A.C.M.

doi:10.1109/tmm.2005.858414

Cited by 47 publications

(21 citation statements)

References 11 publications

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“…The classification system of SafeSurf is more detailed. In order to describe harmfulness of web contents to each age group, it uses eleven categories [2], [8], [5], [10].…”

Section: Pics (Platform For Internet Content Selection)mentioning

confidence: 99%

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Harmful Contents Classification Using the Harmful Word Filtering and SVM

Lee

Chung

et al. 2007

Computational Science – ICCS 2007

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Abstract. As World Wide Web is more popularized nowadays, it is also creating many problems due to uncontrolled flood of information. The pornographic, violent and other harmful information freely available to the youth, who must be protected by the society, or other users who lack the power of judgment or self-control is creating serious social problems. To resolve those harmful words, various methods proposed and studied. This paper proposes and implements the protecting system that protects internet youth user from harmful contents. To effectively classify harmful/harmless contents, this system uses two steps of classification: harmful word filtering and SVM learning based filtering. We achieved result that the average precision of 92.1%.

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“…The classification system of SafeSurf is more detailed. In order to describe harmfulness of web contents to each age group, it uses eleven categories [2], [8], [5], [10].…”

Section: Pics (Platform For Internet Content Selection)mentioning

confidence: 99%

“…One of them is artificial neural network that can learn according to the applied training case. Such learning and adaptation can distinguish the syntax depended words like 'sex' that appears widely in both pornographic and other web pages [10], [11].…”

Section: Intelligent Content Analysismentioning

confidence: 99%

Harmful Contents Classification Using the Harmful Word Filtering and SVM

Lee

Chung

et al. 2007

Computational Science – ICCS 2007

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“…Diffusion of sensitive videos poses a major threat to national security, social stability, and the physical, psychological, and mental health of viewers. Effective recognition of sensitive videos is necessary for web content security [54].…”

Section: Introductionmentioning

confidence: 99%

Multi-Perspective Cost-Sensitive Context-Aware Multi-Instance Sparse Coding and Its Application to Sensitive Video Recognition

Ding

et al. 2016

IEEE Trans. Multimedia

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Abstract:With the development of video-sharing websites, P2P, micro-blog, mobile WAP websites, and so on, sensitive videos can be more easily accessed. Effective sensitive video recognition is necessary for web content security. Among web sensitive videos, this paper focuses on violent and horror videos. Based on color emotion and color harmony theories, we extract visual emotional features from videos. A video is viewed as a bag and each shot in the video is represented by a key frame which is treated as an instance in the bag. Then, we combine multi-instance learning (MIL) with sparse coding to recognize violent and horror videos. The resulting MIL-based model can be updated online to adapt to changing web environments. We propose a cost-sensitive context-aware multi-instance sparse coding (MI-SC) method, in which the contextual structure of the key frames is modeled using a graph, and fusion between audio and visual features is carried out by extending the classic sparse coding into cost-sensitive sparse coding. We then propose a multi-perspective multi-instance joint sparse coding (MI-J-SC) method that handles each bag of instances from an independent perspective, a contextual perspective, and a holistic perspective. The experiments demonstrate that the features with an emotional meaning are effective for violent and horror video recognition, and our cost-sensitive context-aware MI-SC and multi-perspective MI-J-SC methods outperform the traditional MIL methods and the traditional SVM and KNN-based methods.Index terms: Cost-sensitive context-aware MI-SC, Multi-perspective MI-J-SC, Horror video recognition, Violent video recognition, and Video emotional feature extraction.

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“…Previous researchers do adult video detection from the perspective of texts and images [1][2][3][4][5], but little work is from the erotic sound point of view. Adult movies are always accompanied by a series of special sounds, for example groan of females, which are obviously different from normal conversation and other type of sound such as music.…”

Section: Introductionmentioning

confidence: 99%

Study of Objectionable Sound Recognition Based on Histogram Features and SVM

Shi

Gao

Han

et al. 2009

2009 2nd International Congress on Image and Signal Processing

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It is important to protect children from harmful effects of objectionable materials, such as pornography, which are now prevalent on the Internet. In this paper, a new method from the feature porno-sounds recognition point of view is proposed to detect adult video sequences automatically which serves as a complementary approach to the recognition method from image's point of view. To the special of erotic sound, a new set of features based on histograms and contours are introduced for training and classification. Experimental comparisons with popular features for audio classification are presented and discussed. A recognition method is designed based on the standard SVM classification framework due to its extraordinary generalization performance. These efforts result in superior performance that 89.17% recall rate and 10.78% false positive rate are achieved for experiments on real data from the Internet.

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An intelligent categorization engine for bilingual web content filtering

Cited by 47 publications

References 11 publications

Harmful Contents Classification Using the Harmful Word Filtering and SVM

Harmful Contents Classification Using the Harmful Word Filtering and SVM

Multi-Perspective Cost-Sensitive Context-Aware Multi-Instance Sparse Coding and Its Application to Sensitive Video Recognition

Study of Objectionable Sound Recognition Based on Histogram Features and SVM

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