C4.5: programs for machine learning
C4.5: programs for machine learning
Image retrieval by hypertext links
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Automatic caption localization for photographs on World Wide Web pages
Information Processing and Management: an International Journal
Diogenes: a web search agent for person images
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
AMORE: a world-wide web image retrieval engine
CHI '99 Extended Abstracts on Human Factors in Computing Systems
Machine Learning
Image Retrieval from the World Wide Web: Issues, Techniques, and Systems
ACM Computing Surveys (CSUR)
Web image clustering by consistent utilization of visual features and surrounding texts
Proceedings of the 13th annual ACM international conference on Multimedia
Neural Networks: Algorithms and Applications
Neural Networks: Algorithms and Applications
Combining Classifiers for Web Violent Content Detection and Filtering
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007
Using a Semi-automatic Keyword Dictionary for Improving Violent Web Site Filtering
SITIS '07 Proceedings of the 2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Personal image tagging: a game-based approach
Proceedings of the 8th International Conference on Semantic Systems
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The development of the Web has been paralleled by the proliferation of a harmful content on its pages. Using Violent Web images as a case study, we tend to present a novel approach to their classification. This subject is of high importance as it has a potential use in many applications such as violent Web sites filtering. We, therefore, focus our attention on the extraction of contextual image features from the Web page. Also, we present a comparative study of different data mining techniques to classify violent Web images. The results we achieved show that our approach can detect violent content effectively.