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16 January 2006 Style consistent nearest neighbor classifier
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Proceedings Volume 6067, Document Recognition and Retrieval XIII; 60670P (2006) https://doi.org/10.1117/12.643570
Event: Electronic Imaging 2006, 2006, San Jose, California, United States
Abstract
Most pattern classifiers are trained on data from multiple sources, so that they can accurately classify data from any source. However, in many applications, it is necessary to classify groups of test patterns, with patterns in each group generated by the same source. The co-occurring patterns in a group are statistically dependent due to the commonality of source. The dependence between these patterns introduces style context within a group that can be exploited to improve the classification accuracy. In this paper, we present a style consistent nearest neighbor classifier that exploits style context in groups of adjacent patterns to improve the classification accuracy. We demonstrate the efficacy of the proposed classifier on a dataset of machine-printed digits where the proposed classifier reduces the error rate by 64.5%.
© (2006) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Srinivas Andra and Xiaoli Zhang "Style consistent nearest neighbor classifier", Proc. SPIE 6067, Document Recognition and Retrieval XIII, 60670P (16 January 2006); https://doi.org/10.1117/12.643570
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