Fast noise variance estimation
Computer Vision and Image Understanding
Making large-scale support vector machine learning practical
Advances in kernel methods
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
IEEE Intelligent Systems
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Support Vector Machines (SVMs) have been used successfully for many classification tasks In this paper, we investigate applying SVMs to classification in the context of image processing We chose to look at classifying whether pixels have been corrupted by impulsive noise, as this is one of the simpler classification tasks in image processing We found that the straightforward application of SVMs to this problem led to a number of difficulties, such as long training times, performance that was sensitive to the balance of classes in the training data, and poor classification performance overall We suggest remedies for some of these problems, including the use of image filters to suppress variation in the training data This led us to develop a two-stage classification process which used SVMs in the second stage This two-stage process was able to achieve substantially better results than those resulting from the straightforward application of SVMs.