Presentation 2010-09-05
Generic Object Recognition Based on Rough Segmentation Data Given by a User
Mitsuru AMBAI, Yuichi YOSHIDA,
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Abstract(in English) In this paper, we revisit a generic object recognition problem from a point of view of human-computer interaction. Many of existing algorithms of the generic object recognition consist of following three tasks: target region segmentation, feature extraction and classification. While these three tasks are automatically processed on a computer in the previous approaches, solving the target region segmentation task by a computer is not always necessary in many practical situations in which a recognition system can request a user to input rough segmentation data of a target. Although rough segmentation may not give enough classification performance, the use of many rough segmentation datasets in learning process will avoid this problem. In order to validate this hypothesis, we created "20 wild bird datasets" and evaluated classification accuracy of the datasets. Our experiments revealed that generating multiple training samples from a single image by using multiple rough segmentation datasets brought positive effects on classification accuracies, especially when image features including spacial information such as PHOG and PHOW were used.
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Keyword(in English) Generic Object Recognition / User Interface
Paper # PRMU2010-59,IBISML2010-31
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Conference Information
Committee PRMU
Conference Date 2010/8/29(1days)
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Paper Information
Registration To Pattern Recognition and Media Understanding (PRMU)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Generic Object Recognition Based on Rough Segmentation Data Given by a User
Sub Title (in English)
Keyword(1) Generic Object Recognition
Keyword(2) User Interface
1st Author's Name Mitsuru AMBAI
1st Author's Affiliation Denso IT Laboratory, Inc.()
2nd Author's Name Yuichi YOSHIDA
2nd Author's Affiliation Denso IT Laboratory, Inc.
Date 2010-09-05
Paper # PRMU2010-59,IBISML2010-31
Volume (vol) vol.110
Number (no) 187
Page pp.pp.-
#Pages 8
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