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Proceedings Paper

Online image classification under monotonic decision boundary constraint
Author(s): Cheng Lu; Jan Allebach; Jerry Wagner; Brandi Pitta; David Larson; Yandong Guo
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Paper Abstract

Image classification is a prerequisite for copy quality enhancement in all-in-one (AIO) device that comprises a printer and scanner, and which can be used to scan, copy and print. Different processing pipelines are provided in an AIO printer. Each of the processing pipelines is designed specifically for one type of input image to achieve the optimal output image quality. A typical approach to this problem is to apply Support Vector Machine to classify the input image and feed it to its corresponding processing pipeline. The online training SVM can help users to improve the performance of classification as input images accumulate. At the same time, we want to make quick decision on the input image to speed up the classification which means sometimes the AIO device does not need to scan the entire image to make a final decision. These two constraints, online SVM and quick decision, raise questions regarding: 1) what features are suitable for classification; 2) how we should control the decision boundary in online SVM training. This paper will discuss the compatibility of online SVM and quick decision capability.

Paper Details

Date Published: 8 February 2015
PDF: 8 pages
Proc. SPIE 9395, Color Imaging XX: Displaying, Processing, Hardcopy, and Applications, 93950C (8 February 2015); doi: 10.1117/12.2083420
Show Author Affiliations
Cheng Lu, Purdue Univ. (United States)
Jan Allebach, Purdue Univ. (United States)
Jerry Wagner, Hewlett-Packard Co. (United States)
Brandi Pitta, Hewlett-Packard Co. (United States)
David Larson, Hewlett-Packard Co. (United States)
Yandong Guo, Purdue Univ. (United States)


Published in SPIE Proceedings Vol. 9395:
Color Imaging XX: Displaying, Processing, Hardcopy, and Applications
Reiner Eschbach; Gabriel G. Marcu; Alessandro Rizzi, Editor(s)

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