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Trimming Prototypes of Handwritten Digit Images with Subset Infinite Relational Model

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Title: Trimming Prototypes of Handwritten Digit Images with Subset Infinite Relational Model
Authors: Masada, Tomonari / Takasu, Atsuhiro
Issue Date: May-2013
Publisher: Springer Verlag
Citation: Lecture Notes in Electrical Engineering, 240, pp.129-134; 2013
Abstract: We propose a new probabilistic model for constructing efficient prototypes of handwritten digit images. We assume that all digit images are of the same size and obtain one color histogram for each pixel by counting the number of occurrences of each color over multiple images. For example, when we conduct the counting over the images of digit "5", we obtain a set of histograms as a prototype of digit "5". After normalizing each histogram to a probability distribution, we can classify an unknown digit image by multiplying probabilities of the colors appearing at each pixel of the unknown image. We regard this method as the baseline and compare it with a method using our probabilistic model called Multinomialized Subset Infinite Relational Model (MSIRM), which gives a prototype, where color histograms are clustered column- and row-wise. The number of clusters is adjusted flexibly with Chinese restaurant process. Further, MSIRM can detect irrelevant columns and rows. An experiment, comparing our method with the baseline and also with a method using Dirichlet process mixture, revealed that MSIRM could neatly detect irrelevant columns and rows at peripheral part of digit images. That is, MSIRM could "trim" irrelevant part. By utilizing this trimming, we could speed up classification of unknown images.
Description: FTRA 7th International Conference on Multimedia and Ubiquitous Engineering, MUE 2013; Seoul; South Korea; 9 May 2013 through 11 May 2013
Keywords: Bayesian nonparametrics / Classification / Prototype
URI: http://hdl.handle.net/10069/33726
ISSN: 18761100
DOI: 10.1007/978-94-007-6738-6_16
Rights: © 2013 Springer Science+Business Media Dordrecht(Outside the USA)
Type: Conference Paper
Text Version: author
Appears in Collections:Conference Paper

Citable URI : http://hdl.handle.net/10069/33726

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