文章基本信息

标题：SVDDを用いた顕微鏡画像からの新種深海底生物の検出および分類体系上の位置の推定法
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作者：長谷川尭史 ; 小川貴弘 ; 渡邉日出海等
期刊名称：映像情報メディア学会誌
印刷版ISSN：1342-6907
电子版ISSN：1881-6908
出版年度：2012
卷号：66
期号：7
页码：J240-J250
DOI：10.3169/itej.66.J240
出版社：The Institute of Image Information and Television Engineers
摘要：This paper presents a support vector data description (SVDD)-based method for finding new benthic species from microscopic images and its application to taxonomy position estimation. First, the proposed method generates hyperspheres that represent taxonomic species taxa of known species and enables automatic species classification. Furthermore, weight estimation of visual features based on multiple kernel learning (MKL) is used in this approach to realize automatic weighting of categorical traits that are traditionally determined by taxonomists. Next, based on the traditional taxonomic classification scheme, the proposed method merges the hyperspheres of similar species and generates new hyperspheres that represent ultra-species taxa in higher hierarchies. Then, from the obtained results, a new decision tree, whose nodes are hyperspheres of species taxa and ultra-species taxa, is constructed. By using this decision tree, new benthic species can be found from target samples, and their taxonomic positions can also be estimated.
关键词：SVDD;決定木;生物多様性;新種生物探索;体系分類