IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-Organizing Maps
Statistical color models with application to skin detection
International Journal of Computer Vision
Classifying Objectionable Websites Based on Image Content
IDMS '98 Proceedings of the 5th International Workshop on Interactive Distributed Multimedia Systems and Telecommunication Services
Detection of Interest Points for Image Indexation
VISUAL '99 Proceedings of the Third International Conference on Visual Information and Information Systems
Non-retrieval: Blocking Pornographic Images
CIVR '02 Proceedings of the International Conference on Image and Video Retrieval
WACV '96 Proceedings of the 3rd IEEE Workshop on Applications of Computer Vision (WACV '96)
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Modeling Scenes with Local Descriptors and Latent Aspects
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Multimedia Tools and Applications
Handwritten digit recognition by adaptive-subspace self-organizing map (ASSOM)
IEEE Transactions on Neural Networks
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This article presents a method aiming at filtering objectionable image contents. This kind of problem is very similar to object recognition and image classification. In this paper, we propose to use Adaptive-Subspace Self-Organizing Maps (ASSOM) to generate invariant pornographic features. To reach this goal, we construct local signatures associated to salient patches according to adult and benign databases. Then, we feed these vectors into each specialized ASSOM neural network. At the end of the learning step, each neural unit is tuned to a particular local signature prototype. Thus, each input image generates two neural maps that can be represented by two activation vectors. A supervised learning is finally done by a Normalized Radial Basis Function (NRBF) network to decide the image category. This scheme offers very promising results for image classification with a percentage of 87.8% of correct classification rates.