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
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
A comprehensive approach to image spam detection: from server to client solution
IEEE Transactions on Information Forensics and Security
A survey of image spamming and filtering techniques
Artificial Intelligence Review
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Image spam is annoying email users around the world. Most previous work for image spam detection focuses on supervised learning approaches. However, it is costly to get enough trustworthy labels for learning, especially for an adversarial problem where spammers constantly modify patterns to evade the classifier. To address this issue, we employ the principle of active learning where the learner guides the user to label as few images as possible while maximizing the classification accuracy. Active learning is more suited for online image spam filtering since it dramatically reduces the labeling costs with negligible overhead while maintaining high recognition performance. We present and compare two active learning algorithms, based on an SVM and a Gaussian process classifier respectively. To the best of our knowledge, we are the first to apply active learning for the task of spam image filtering. Experimental results demonstrate that our active learning based approaches quickly achieve 99% high detection rate and