Presentation 2010-05-14
An Analysis of the Impact of a Training Dataset Expansion for Generic Object Recognition
Takumi TOYAMA, Koichi KISE,
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Abstract(in English) In the field of pattern recognition, it is well known that the contents of a training dataset affects the recognition performance. Most datasets used in the field of generic object recognition have a somewhat small number of samples per category. Thus, it is reasonable to think that we can acquire higher recognition performance by expanding the dataset. Although several approaches have attempted to increase the number of samples, the amount of these increased samples is about ten thousands which can be increased more to acquire much higher recognition performance. Thus, we collect hundred thousands of images and compare the recognition performance between the dataset with original size and the dataset which is expanded with our dataset expansion method. In addition, we also analyze the impact of using different classifiers for these expanded datasets.
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Keyword(in English) Generic object recognition / Dataset expansion / Filtering / Support Vector Machine / k-Nearest Neighbor
Paper # IE2010-40,PRMU2010-28,MI2010-28
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Committee MI
Conference Date 2010/5/6(1days)
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Registration To Medical Imaging (MI)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) An Analysis of the Impact of a Training Dataset Expansion for Generic Object Recognition
Sub Title (in English)
Keyword(1) Generic object recognition
Keyword(2) Dataset expansion
Keyword(3) Filtering
Keyword(4) Support Vector Machine
Keyword(5) k-Nearest Neighbor
1st Author's Name Takumi TOYAMA
1st Author's Affiliation Graduate School of Engineering, Osaka Prefecture University()
2nd Author's Name Koichi KISE
2nd Author's Affiliation Graduate School of Engineering, Osaka Prefecture University
Date 2010-05-14
Paper # IE2010-40,PRMU2010-28,MI2010-28
Volume (vol) vol.110
Number (no) 28
Page pp.pp.-
#Pages 6
Date of Issue