From the July 2013 issue
Writer Adaptation with Style Transfer Mapping
By Xu-Yao Zhang and Cheng-Lin Liu
Adapting a writer-independent classifier toward the unique handwriting style of a particular writer has the potential to significantly increase accuracy for personalized handwriting recognition. This paper proposes a novel framework of style transfer mapping (STM) for writer adaptation. The STM is a writer-specific class-independent feature transformation which has a closed-form solution. After style transfer mapping, the data of different writers are projected onto a style-free space, where the writer-independent classifier needs no change to classify the transformed data and can achieve significantly higher accuracy. The framework of STM can be combined with different types of classifiers for supervised, unsupervised, and semi-supervised adaptation, where writer-specific data can be either labeled or unlabeled and need not cover all classes...
Editorials and Announcements
- We are pleased to announce that David Forsyth, a professor at the University of Illinois at Urbana-Champaign, is the new Editor in Chief of IEEE Transactions on Pattern and Machine Intelligence starting in 2013. He was previously a member of the advisory board of TPAMI.
- Print on Demand is Now Available for OnlinePlus Titles
- eBooks of issues of TPAMI can now be downloaded from the Computer Society Digital Library
- TPAMI Essential Set now available
- Editor's Note (June 2013)
- Farewall State of the Journal (Jan 2013)
- Editor's Note (Jan 2013)
- Editor's Note (May 2012)
- Editor's Note (February 2012)
- State of the Journal (January 2012)
- In Memoriam: Mark Everingham (Nov 2012)
- Introduction to the Special Section on IEEE Conference on Computer Vision and Pattern Recognition (September 2012)
Call for Papers
- Special Issue on "Higher Order Graphical Models in Computer Vision: Modelling, Inference and Learning" (PDF)
Submission deadline: June 1, 2013.
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IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) is a scholarly archival journal published monthly. This journal covers traditional areas of computer vision and image understanding, all traditional areas of pattern analysis and recognition, and selected areas of machine intelligence.
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