A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information-based objective functions for active data selection
Neural Computation
Machine Learning
Selective Sampling Using the Query by Committee Algorithm
Machine Learning
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Support Vector Machine Active Learning with Application sto Text Classification
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Image Indexing Using Color Correlograms
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Multimodal concept-dependent active learning for image retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Regularized locality preserving indexing via spectral regression
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Detecting image spam using visual features and near duplicate detection
Proceedings of the 17th international conference on World Wide Web
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Semi Supervised Image Spam Hunter: A Regularized Discriminant EM Approach
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Active Learning Image Spam Hunter
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
Support vector machines for spam categorization
IEEE Transactions on Neural Networks
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Image spam is a type of e-mail spam that embeds spam text content into graphical images to bypass traditional text-based e-mail spam filters. To effectively detect image spam, it is desirable to leverage image content analysis technologies. However, most previous works of image spam detection focus on filtering the image spam on the client side. We propose a more desirable comprehensive solution which embraces both server-side filtering and client-side detection to effectively mitigate image spam. On the server side, we present a nonnegative sparsity induced similarity measure for cluster analysis of spam images to filter the attack activities of spammers and fast trace back the spam sources. On the client side, we employ the principle of active learning where the learner guides the users to label as few images as possible while maximizing the classification accuracy. The server-side filtering identifies large image clusters as suspicious spam sources and further analysis can be performed to identify the real sources and block them from the beginning. For those spam images which survived the server-side filter, our active learner on the client side will further guide the users to interactively and efficiently filter them out. Our experiments on an image spam data-set collected from the e-mail server of our department demonstrate the efficacy of the proposed comprehensive solution.