Flower Classification by Using Multiple Kernel Learning

言語	英語
種類	国際会議等の予稿
著者	Tomohiro Sakata，Koichi Kise
論文名	Flower Classification by Using Multiple Kernel Learning
論文誌名	Proceedings of The 2nd China-Japan-Korea Joint Workshop on Pattern Recognition (CJKPR2010)
ページ	pp.144-147
査読の有無	有
年月	2010年11月
分野	パターン認識, 物体認識
キー	`Sakata2010` (id=1171)

要約

Object classification for categories with a significant visual similarity is a difficult problem. Because natural objects are slightly different for each individual, it is difficult to classify them with one feature. Therefore multiple features are needed for classification. As a method of combining multiple features, MKL has recently been focused. In this research, we employ color, shape, and texture features. We classify the flower images by using MKL and investigate the recognition rate. As a result, the best recognition rate is 75.66% in combining three features with flower 17 category dataset published by Visual Geometry Group of Oxford University.

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出力結果

@inproceedings{Sakata2010, author = {Sakata, Tomohiro and Kise, Koichi}, title = {Flower Classification by Using Multiple Kernel Learning}, booktitle = {Proceedings of The 2nd China-Japan-Korea Joint Workshop on Pattern Recognition (CJKPR2010)}, year = 2010, month = nov, pages = {144--147} }

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Flower Classification by Using Multiple Kernel Learning

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