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Trimming Prototypes of Handwritten Digit Images with Subset Infinite Relational Model
http://hdl.handle.net/10069/33726
http://hdl.handle.net/10069/3372602b2b6a7-e4b9-41d4-beec-565f727b66dd
名前 / ファイル | ライセンス | アクション |
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LNEE240_129.pdf (379.1 kB)
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Item type | 会議発表論文 / Conference Paper(1) | |||||
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公開日 | 2014-06-01 | |||||
タイトル | ||||||
タイトル | Trimming Prototypes of Handwritten Digit Images with Subset Infinite Relational Model | |||||
言語 | ||||||
言語 | eng | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | Bayesian nonparametrics | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | Classification | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | Prototype | |||||
資源タイプ | ||||||
資源タイプ識別子 | http://purl.org/coar/resource_type/c_5794 | |||||
資源タイプ | conference paper | |||||
著者 |
Masada, Tomonari
× Masada, Tomonari× Takasu, Atsuhiro |
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抄録 | ||||||
内容記述タイプ | Abstract | |||||
内容記述 | 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. | |||||
内容記述 | ||||||
内容記述タイプ | Other | |||||
内容記述 | FTRA 7th International Conference on Multimedia and Ubiquitous Engineering, MUE 2013; Seoul; South Korea; 9 May 2013 through 11 May 2013 | |||||
書誌情報 |
Lecture Notes in Electrical Engineering 巻 240, p. 129-134, 発行日 2013-05 |
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ISSN | ||||||
収録物識別子タイプ | ISSN | |||||
収録物識別子 | 18761100 | |||||
EISSN | ||||||
収録物識別子タイプ | ISSN | |||||
収録物識別子 | 18761119 | |||||
DOI | ||||||
関連タイプ | isVersionOf | |||||
識別子タイプ | DOI | |||||
関連識別子 | 10.1007/978-94-007-6738-6_16 | |||||
権利 | ||||||
権利情報 | © 2013 Springer Science+Business Media Dordrecht(Outside the USA) | |||||
著者版フラグ | ||||||
出版タイプ | AM | |||||
出版タイプResource | http://purl.org/coar/version/c_ab4af688f83e57aa | |||||
出版者 | ||||||
出版者 | Springer Verlag | |||||
引用 | ||||||
内容記述タイプ | Other | |||||
内容記述 | Lecture Notes in Electrical Engineering, 240, pp.129-134; 2013 |