C4.5: programs for machine learning
C4.5: programs for machine learning
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinguishing photographs and graphics on the World Wide Web
CAIVL '97 Proceedings of the 1997 Workshop on Content-Based Access of Image and Video Libraries (CBAIVL '97)
Histogram refinement for content-based image retrieval
WACV '96 Proceedings of the 3rd IEEE Workshop on Applications of Computer Vision (WACV '96)
Mixing virtual and real scenes in the site of ancient Pompeii: Research Articles
Computer Animation and Virtual Worlds
Device-based decision-making for adaptation of three-dimensional content
The Visual Computer: International Journal of Computer Graphics
Defect image classification and retrieval with MPEG-7 descriptors
SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis
Fusing MPEG-7 visual descriptors for image classification
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Neural network based adult image classification
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
Multi class adult image classification using neural networks
AI'05 Proceedings of the 18th Canadian Society conference on Advances in Artificial Intelligence
The MPEG-7 visual standard for content description-an overview
IEEE Transactions on Circuits and Systems for Video Technology
MPEG-7 visual shape descriptors
IEEE Transactions on Circuits and Systems for Video Technology
PicSOM-self-organizing image retrieval with MPEG-7 content descriptors
IEEE Transactions on Neural Networks
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Utilizing Virtual Reality technologies for virtual museum brings new ways of interactive presentation of the contents. In addition to interactivity, personalization is an important emerging issue in digital content management especially with virtual reality. For the virtual museum or gallery, selection and presentation of personalized content will improve user experience in navigating through huge collections like Musée du Louvre or British Museum. In this paper, we present a personalization method of massive multimedia content in virtual galleries. The proposed method is targeted for the pictures that could be characterized by its large amount of source in galleries. The method is based on classified image features which are extracted using standard MPEG-7 visual descriptors. Using Neural Networks, the best matching pictures are selected and presented in the virtual gallery by choosing similar styles from the extracted preference features. The simulation results show that the proposed system successfully classifies images into correct classes with the rate of over 75% depending on the employed features. We employ the result into a virtual gallery application which gives opportunities of automatically personalized gallery browsing.